50 jobs found

Junior DevOps / MLOps Engineer We are looking for a Junior DevOps / MLOps Engineer to help build, automate and maintain the infrastructure and pipelines that deliver software and machine learning models into production. Working alongside software and AI engineers, you will support reliable deployments, improve automation and help ensure production systems operate effectively as demand grows. This role would suit an early-career engineer with a foundation in cloud computing, scripting or software development and an interest in machine learning infrastructure. Key Responsibilities Support the configuration and maintenance of AWS infrastructure for software applications and machine learning workloads. Help build and maintain CI/CD pipelines using GitHub and associated automation tools. Automate routine deployment, configuration and operational tasks. Support the packaging, versioning and deployment of machine learning models. Monitor infrastructure and deployment pipelines, investigate issues and help implement fixes. Work with developers to maintain consistent development, testing and production environments. Follow good practices for access management, secrets, security and documentation. Use GitHub Copilot to support development and automation, reviewing and testing generated code carefully. Essential Skills and Experience Practical experience gained through employment, an apprenticeship, university projects or personal development. Familiarity with Git and GitHub for version control and collaborative development. A basic understanding of AWS services and cloud infrastructure. Some scripting or programming experience, ideally Python, Bash or PowerShell. An understanding of automated testing and software deployment principles. Strong problem-solving skills and a willingness to learn. An interest in how machine learning models are deployed, monitored and maintained. Desirable Experience GitHub Actions or another CI/CD tool. Linux administration and Docker containers. Infrastructure as code, such as Terraform or AWS CloudFormation. Exposure to model versioning, experiment tracking or MLOps tools. Experience using GitHub Copilot. What You Will Contribute You will help create repeatable deployment processes, reduce manual work and improve the reliability of software and machine learning systems in production. Benefits: Hybrid / Remote working Flexible working + other benefits Interested? Please Click Apply Now! Junior DevOps / MLOps Engineer

I'm working on an exciting AI engineering project with a leading enterprise focused on deploying agentic GenAI solutions in AWS. Key responsibilities: Deploy and operationalise GenAI solutions in AWS Build highly available SageMaker endpoints with monitoring and alerting Develop Terraform modules for repeatable infrastructure deployments Implement CI/CD and MLOps best practices Produce architecture documentation, configuration guides and operational runbooks Deliver knowledge transfer sessions and support engineering handover We're looking for someone with: Generative AI / Agentic AI experience (essential) Strong AWS experience, ideally Bedrock MLOps and production ML deployments Terraform and Infrastructure as Code CI/CD and DevOps practices Monitoring, alerting and operational support Location:Remote (UK based candidates only) Contract length - 6 months Daily rate - £550 (inside IR35) Start Date: ASAP Must be elligible to pass SC or BPSS

We're partnering with an organisation delivering a cutting-edge AI platform project. This is an opportunity to join a highly technical team building the cloud infrastructure and platform capabilities that power large-scale AI and machine learning solutions. You'll play a key role in designing, building and maintaining scalable, secure and resilient platform infrastructure, enabling engineering and data teams to deploy and operate AI workloads efficiently. What you'll be doing: Building and managing cloud-native infrastructure to support AI and ML applications. Designing and maintaining Kubernetes environments for production workloads. Developing and managing Infrastructure as Code using Terraform. Supporting CI/CD pipelines and platform automation initiatives. Improving platform reliability, observability and scalability. Collaborating closely with Software Engineers, Data Engineers and ML teams to optimise deployment workflows and infrastructure performance. Essential experience: Strong commercial experience with Kubernetes. Hands-on experience building Infrastructure as Code using Terraform. Experience working with cloud platforms such as AWS, Azure or GCP. Experience with containerisation technologies including Docker. Strong understanding of CI/CD principles and DevOps best practices. Experience supporting highly available, scalable production environments. Nice to have: Experience with Kubeflow and ML platform tooling. Exposure to AI, machine learning or GenAI projects. Experience with observability tooling such as Prometheus, Grafana or OpenTelemetry. Experience working within regulated or enterprise environments. This is a fantastic opportunity to join a high-profile AI programme with significant investment and visibility, working alongside some of the brightest engineers in the industry to build the next generation of AI infrastructure. 49914MSR2 INDLON The Portfolio Group are acting on behalf of our client in recruiting for this position.

We're partnering with an organisation delivering a cutting-edge AI platform project. This is an opportunity to join a highly technical team building the cloud infrastructure and platform capabilities that power large-scale AI and machine learning solutions. You'll play a key role in designing, building and maintaining scalable, secure and resilient platform infrastructure, enabling engineering and data teams to deploy and operate AI workloads efficiently. What you'll be doing: Building and managing cloud-native infrastructure to support AI and ML applications. Designing and maintaining Kubernetes environments for production workloads. Developing and managing Infrastructure as Code using Terraform. Supporting CI/CD pipelines and platform automation initiatives. Improving platform reliability, observability and scalability. Collaborating closely with Software Engineers, Data Engineers and ML teams to optimise deployment workflows and infrastructure performance. Essential experience: Strong commercial experience with Kubernetes. Hands-on experience building Infrastructure as Code using Terraform. Experience working with cloud platforms such as AWS, Azure or GCP. Experience with containerisation technologies including Docker. Strong understanding of CI/CD principles and DevOps best practices. Experience supporting highly available, scalable production environments. Nice to have: Experience with Kubeflow and ML platform tooling. Exposure to AI, machine learning or GenAI projects. Experience with observability tooling such as Prometheus, Grafana or OpenTelemetry. Experience working within regulated or enterprise environments. This is a fantastic opportunity to join a high-profile AI programme with significant investment and visibility, working alongside some of the brightest engineers in the industry to build the next generation of AI infrastructure. 49914MSR2 INDLON The Portfolio Group are acting on behalf of our client in recruiting for this position.

Location: Glasgow, United Kingdom Thales is a global technology leader with more than 83,000 employees on five continents. With over 7,500 people in the UK, operating across defence, space, aerospace, and digital security, we help build a future we can all trust. Thales supports the security and stability of our nation by providing extraordinary technology to our customers, as well as delivering social value to the UK with our products and services. 2027 AI Engineer Apprentice - AI and Data Science Graduate Level Apprenticeship Start Date: 06 September 2027 Salary: £24,000 per annum Employee type: Permanent Location: Glasgow Working Schedule: Monday - Friday, 37 hours a week (8 hours Monday - Thursday, 5 hours Friday) Turn your curiosity into a career. If you're passionate about artificial intelligence, eager to learn, and looking for a degree apprenticeship that combines academic study with exciting on-the-job experience, we'd love to hear from you. Benefits of the programme: Earn a salary while studying for your qualification Gain hands-on experience in a real AI and engineering environment Receive job-specific training and development Achieve a recognised Level 6 qualification Secure a permanent role from the start of your apprenticeship Have your university tuition fees fully paid About the Role As an AI Engineer Apprentice, you will develop the technical knowledge, practical skills and professional behaviours needed to design, build, test and deploy dependable AI-enabled solutions. Working as part of our AI and engineering community, you will contribute to live projects across the machine learning lifecycle, collaborating with software, data, systems and domain specialists to turn needs into secure, maintainable solutions. Your responsibilities may include: Preparing, exploring and validating data for machine learning use cases Developing and evaluating machine learning models and AI-enabled software components Writing clean, tested and documented code using appropriate engineering tools and practices Supporting model integration, deployment, monitoring and performance improvement Contributing to technical reviews, experiments and structured problem solving Working with experienced engineers and researchers on live projects What You Will Learn Throughout the apprenticeship, you will gain experience and knowledge in: Professional engineering behaviours, teamwork and industry best practice Programming, software engineering, version control and automated testing Data preparation, feature engineering and data quality techniques Machine learning methods, model selection, training and evaluation Deployment patterns, MLOps, monitoring and lifecycle management Cloud, compute and development tools used to deliver AI solutions Responsible AI, security, privacy, safety and regulatory considerations Structured approaches to troubleshooting, experimentation and continuous improvement Degree Apprenticeship Programme You will study towards an AI and Data Science Graduate Level Apprenticeship qualification with Glasgow Caledonian University, developing the knowledge, skills and behaviours required to build and apply artificial intelligence responsibly in an engineering environment. The programme is delivered through a blended learning approach, combining: On campus study Live virtual lectures Online learning resources Self-directed study Programme structure: The BSc (Hons) AI and Data Science (Graduate Apprenticeship) will equip you with the knowledge and skills required to succeed in your role within Thales. Providing a strong foundation across data and AI, this programme will help you to learn how to manage, explore, analyse, understand and visualise data, alongside technical skills such as software development and machine learning. Delivered over 4 years, you will achieve: Year 1: Certificate of Higher Education in Computing Year 2: Diploma of Higher Education in Computing Year 4: BSc (Hons) AI and Data Science (Graduate Apprenticeship) Entry Requirements To apply, you will need: Highers 96 UCAS points Four Highers (BBCC) including Maths or Computing A foundation apprenticeship will be equal to one Higher at B A modern apprenticeship SCQF Level 6 in a relevant discipline will be equal to two Highers at B Benefits Competitive salary of £24,000 per annum Performance-related pay uplifts Holiday entitlement of 201 hours a year (plus a company day and bank holidays) Contributory Company Pension Health Care Cash Plan Hybrid/flexible working options Employee assistance programme Learning and development support Employee discounts and wellbeing initiatives Life Cover 80 Hours Volunteering per year (first two years) Thales Apprentice Recruitment Process 1. Apply Online - submit your application online to register your interest. You only need to register for one role as you will have the opportunity to apply for additional roles at the full application stage. 2. Upload Your CV 3. Check your emails for the invitation to apply - this will comefrom 4. Complete Your Application - follow the link provided in the email to complete your application. You can apply for one primary role and up to 3 additional roles. 5. Complete Online Tests - undertake the online assessments designed to evaluate your suitability for the role. 6. Virtual Assessment Centre - candidates who progress from the online assessment stage will attend a virtual assessment centre, which will include group exercises, and competency-based interviews. 7. Job Offer - successful candidates who perform well throughout the recruitment process will receive a job offer from Thales. For further information about our Future Talent recruitment process, including hints and tips, or to connect with a member of the Future Talent Team, a Graduate, or an Apprentice, please visit Thales Future Talent - Connectr . Please note, assessment centres for shortlisted candidates will be held between February - May 2027. Induction week for all Graduate and Apprentices will take place from Monday 6 September - Friday 10 September 2027. Closing date: Our applications will close at 11.59pm on Wednesday 17 February 2027, this is the only time we will be accepting applications this year. Please note, we will be in contact by the end of February 2027 with details of next steps. PLEASE NOTE THAT ROLES MAY CLOSE EARLIER THAN THE ADVERTISED DEADLINE IF WE RECEIVE A HIGH NUMER OF APPLICATIONS. WE ENCOURAGE YOU TO APPLY AS SOON AS POSSIBLE TO AVOID MISSING OUT ON THIS OPPORTUNITY. Because of the work we do at Thales, all roles are subject to security requirements. To be considered for this position, you must have the permanent right to work in the UK and be able to successfully complete and maintain UK Government security checks, including Baseline Personnel Security Standard (BPSS) screening and Security Clearance (SC), which is a government background check, before starting employment. To be eligible for SC clearance, you will typically need to have lived in the UK continuously for the last five years. As part of the vetting process, you will be asked to provide evidence of your identity, right to work in the UK, any time spent overseas during the last five years, and your employment and/or education history. For further details of the evidence required to apply for security clearance please follow this link - . Thales is an equal opportunities employer and diversity, and inclusion are integral to the success of Thales. At Thales, we ensure equal opportunities, pay and working conditions for all. The benefits we offer include private medical insurance, buying or selling annual leave, cycle to work schemes, employee discounts, paid volunteering day, stocks and shares, annual bonus and much more depending on the role. Read more about our benefits here. We are committed to creating a workplace where everyone feels valued for who they are and the unique strengths they bring. Discover more about our programmes, employee networks, wellbeing policies, and inclusive features here. If this role isn't quite right for you, we encourage you to join our talent community where your details will be shared with our recruitment teams for other potential opportunities. Join the Talent Community here. Join Thales in the UK - Innovate with us and shape the future!

Job Title: Engineering Manager, AI Company: 167 Solutions Location: Hybrid (UK ONLY) Salary: £150,000 - £170,000 per annum Contract Type: Permanent Experience Level: Minimum 5 years' experience Start Date: ASAP The Opportunity: 167Solutions is partnering with a globally recognised media and information services organisation to recruit an Engineering Manager - Applied AI & Data Science . Our client is at the forefront of digital innovation, investing heavily in artificial intelligence, machine learning and data science to transform how millions of people access and interact with trusted information. The role combines people leadership, technical architecture and hands-on engineering, with a particular focus on generative AI, agentic systems, LLMOps and AI-native software development. You'll play a key role in shaping the organisation's AI capabilities, delivering production-grade AI systems and driving the adoption of modern AI engineering practices. The Role: As Engineering Manager, you will lead the Applied AI and Data Science team, taking ownership of technical delivery, team development and engineering standards while remaining actively involved in designing and building AI solutions. You will work closely with senior AI leadership, product managers, data engineers and business stakeholders to translate ambitious AI initiatives into scalable, secure and commercially valuable solutions. The team's work spans large language models, agentic AI, retrieval-augmented generation (RAG), natural language processing, predictive modelling and shared AI platform capabilities. What You'll Do: Lead and develop a multidisciplinary AI team , providing day-to-day management, mentorship, technical guidance and career development for AI/ML Engineers, NLP Engineers and Data Scientists. Own technical delivery , managing priorities, resources and engineering commitments while balancing rapid experimentation with production requirements. Remain hands-on , contributing Python code, reviewing models and implementations, prototyping solutions and supporting complex technical challenges. Shape AI architecture , guiding the design and development of agentic AI systems, RAG pipelines, NLP solutions and machine learning applications. Build safe and reliable AI agents , implementing appropriate guardrails, tool permissions, human-in-the-loop controls and end-to-end evaluation. Drive AI-native software engineering , introducing and scaling the use of tools such as Claude Code, Cursor and GitHub Copilot across development, testing and code review. Establish engineering best practices , covering LLMOps, MLOps, CI/CD, automated evaluation, observability, security, performance and cost optimisation. Work extensively with AWS AI services , including Amazon Bedrock, Bedrock AgentCore, SageMaker, Lambda and Step Functions. What We Are Looking For: Proven experience as an Engineering Manager or Technical Lead with direct people-management responsibility for AI Engineers, ML Engineers, NLP Engineers or Data Scientists. Strong hands-on AI/ML engineering background, with advanced Python skills and experience developing production-grade AI systems. Experience designing and building LLM-powered and agentic AI applications, including RAG, NLP and traditional machine learning solutions. Practical experience with modern agent frameworks such as LangGraph, Strands Agents or Claude Agent SDK, alongside interoperability standards including MCP and A2A. Strong AWS cloud engineering experience, particularly Amazon Bedrock, Bedrock AgentCore, SageMaker, Lambda and Step Functions, with Infrastructure as Code using AWS CDK or equivalent. Hands-on experience with LLMOps and MLOps, including model and agent evaluation, trajectory testing, tracing, observability, guardrails and cost monitoring. Understanding of AI security challenges, including prompt injection, data leakage, excessive agent permissions and appropriate mitigation strategies. Experience using AI coding assistants and agents, such as Claude Code, Cursor or GitHub Copilot, throughout the software development lifecycle. Ability to establish engineering standards for AI-generated code, context engineering, automated testing and AI-assisted development. Desirable Experience: Experience building and operating production-grade agentic AI systems, including multi-agent orchestration, agent memory, MCP-based tool integration and agentic RAG. Strong NLP expertise, including semantic search, summarisation, information extraction, classification or LLM fine-tuning. Experience developing shared AI platforms, internal MCP servers or reusable AI capabilities used by multiple engineering teams. Knowledge of vector search technologies such as OpenSearch, Amazon S3 Vectors or pgvector. Familiarity with AI governance, regulatory considerations and responsible AI development practices. Why Join Us: This is an opportunity to take a leading role in a major AI transformation programme within a globally established, highly respected organisation. You'll be working on meaningful AI initiatives with the potential to impact millions of users, while helping shape the technical direction and engineering culture of a growing Applied AI and Data Science function. The position offers a rare combination of hands-on technical ownership, people leadership and strategic influence, giving you the opportunity to work with cutting-edge agentic AI technologies while building and developing a high-performing engineering team. If you're an experienced AI Engineering Manager who wants to remain close to the technology, lead talented engineers and data scientists, and help deliver the next generation of AI-powered products, we'd love to hear from you.

Job Title Data Scientist Job Description Job purpose: Our IT mission is to transform how data and AI are delivered to customers and colleagues around the world, with a focus on simplicity, trust, and measurable value. AI is central to this mission, providing tools and platforms for creating and operating secure, scalable data and AI solutions. Reporting to the BI and Analytics Manager, the Data Scientist is a valued member of the Enterprise Analytics & AI Hub. This role develops and evaluates advanced analytics, machine learning, and generative AI solutions that help us understand business challenges, improve decisions, and create better customer and colleague experiences. Using modern data science techniques and scalable AWS technologies, the Data Scientist connects business needs with reliable technical solutions. This role works closely with product, data engineering, software and platform engineering, security, governance, and domain experts to move ideas from discovery and experimentation into monitored production services. The Data Scientist plays a vital role within the Enterprise Analytics & AI Hub, contributing to the team's core objectives: • Turn ambiguous business questions into measurable data science problems, clear success measures, and evidence-based recommendations. • Build trusted models, data products, and AI capabilities in Nexus (our internal platform) that colleagues can use safely and effectively. • Improve the quality and usability of structured and unstructured data by making definitions, lineage, assumptions, and limitations clear. • Establish evaluation, observability, and feedback practices that improve accuracy, reliability, latency, and cost over time. • Foster a culture of responsible, data-informed experimentation by sharing insights, reusable approaches, and emerging techniques across the team. Key Responsibilities Key Responsibilities: Partner with product owners, domain experts, and stakeholders to translate business challenges into measurable analytical or AI outcomes, data requirements, and evaluation plans. Explore, prepare, and validate structured and unstructured data, establishing appropriate quality checks, feature definitions, lineage, and documented limitations. Design, develop, and validate statistical and machine learning solutions for tasks such as classification, regression, clustering, forecasting, segmentation, and optimization. Design experiments and apply statistical analysis, including A/B testing where appropriate, to measure impact, interpret uncertainty, and support sound decisions. Build and evaluate generative AI capabilities within Nexus (AWS stack), including prompt and context design, embeddings, retrieval-augmented generation, knowledge bases, and agent or tool behaviours. Write production-quality Python and SQL, and work with engineers to integrate successful solutions into scalable AWS services, data pipelines, and APIs. Apply MLOps and LLMOps practices, including version control, automated tests, CI/CD, reproducible deployment, monitoring, drift detection, and rollback. Define and maintain offline and live evaluation measures, using traces, token usage, cost, performance, and user feedback to improve solutions over time. Apply responsible AI, privacy, security, and data governance principles, with appropriate access controls, explainability, safety checks, and human oversight. Communicate insights, trade-offs, and recommendations clearly to technical and business audiences, and contribute reusable patterns, documentation, and learning across the team. Function IT Country United Kingdom Office Location Newcastle Work Place type Hybrid Advert Working at Sage means you're supporting millions of small and medium sized businesses globally with technology to work faster and smarter. We leverage the future of AI, meaning business owners spend less time doing routine tasks, like entering invoices and generating reports, and more time pursuing their ambitions. Our colleagues are the best of the best. It's why we were awarded 2024 Best Places to Work by Glassdoor. Because to achieve extraordinary outcomes, we need extraordinary teams. This means infusing Sage with people who knock down barriers, continuously innovate, and want to experience their potential. Learn more about working at Sage: Watch a video about our culture: v=qIoiCpZH-QE We celebrate individuality and welcome you to join us if you embrace all backgrounds, identities, beliefs, and ways of working. If you need support applying, reach out at . Learn more about DEI at Sage:

Waracle are looking for a Lead Devops Engineer for an exciting new role joining our world-class digital technology consultancy and home to a diverse, smart, curious and ambitious community of specialists in technology-driven transformation. We work with ambitious clients to help them solve their biggest business and customer challenges. We help our clients to innovate and create intelligent digital products and services. We thrive on complex challenges and deliver business-critical IT transformation projects, moving seamlessly from strategy, design and delivery to operations. This is a Hybrid role working from our Glasgow, Dundee or London office 2 days a week. As a Lead DevOps Engineer, you ll serve as an empathetic leader, strategic advisor, and technical authority across our engineering squads. Bringing a broad, generalist mindset across platform engineering and modern infrastructure, you will lead the integration of AI-driven delivery practices, intelligent automation, and Generative AI tooling into everyday workflows. You will define project-level technical architecture, mentor and lift up growing engineers, and nurture a collaborative, psychologically safe environment where everyone feels empowered to innovate. Balancing short-term delivery with long-term maintainability, you will play a key role in de-escalating conflicts, aligning client stakeholders, and driving high-impact technical strategy aligned with human-centric business goals. Key aspects of the role include Technical Strategy & AI Integration: Defining technical architecture across squads, anticipating system scaling risks, and driving the adoption of modern AI tooling, MLOps, and intelligent automation within cloud environments. Broad Generalist Leadership: Leveraging your breadth of engineering knowledge to bridge gaps between software delivery, platform operations, and data infrastructure, making complex trade-offs clear and explicit for clients. People & Culture: Creating an inclusive, psychologically safe team environment, regularly coaching team members with constructive feedback, and balancing delivery pace with overall team wellbeing. Standards & Compliance: Guiding teams on performance strategies, proactive optimisation, and security standards (such as OWASP, GDPR, SOC2), ensuring responsible AI practices, clean architecture, and secure code execution. Stakeholder Collaboration: Facilitating workshops to discover requirements, managing project scope and expectations, and translating client priorities into actionable technical strategy. Innovation & Influence: Evaluating emerging AI tools and technical approaches through tech spikes, shaping go-to-market strategies for new offerings, driving delivery tooling improvements, and representing Waracle at external tech events. Research shows that underrepresented groups tend to only apply for roles that they believe they meet 100% of the criteria for. At Waracle, we want to buck that trend! We hire for passion, potential and mindset as much as credentials. If you think you'd be incredible in the role, then apply - even if you don't think you tick every box. Job requirements What you'll bring A versatile, generalist engineering background with broad knowledge across cloud platforms, CI/CD, containerisation, and infrastructure-as-code. Practical exposure to or enthusiasm for integrating Generative AI, AI-assisted development tools, or modern MLOps pipelines into delivery streams. Proven experience guiding technical direction and architecture for cloud and platform solutions. A passion for mentorship, coaching, and creating supportive pathways for engineers at all career stages. Commitment to fostering inclusive, collaborative team dynamics that prioritise wellbeing alongside quality outcomes. Strong understanding of security, accessibility, and compliance standards (OWASP, GDPR, SOC2) in modern cloud architectures. Excellent communication skills with an aptitude for breaking down complex technical concepts for diverse stakeholders. Familiarity with tracking key engineering metrics (such as DORA) to guide continuous, data-informed improvement. What to expect from our hiring process 1. First Hello: An informal chat with your dedicated Talent Attraction Partner to talk about Waracle, what you want out of your next move, salary, benefits, and office hybrid working model. 2. Talking Impact: A collaborative deep dive into how you solve problems, work with others, and raise the bar, guided by our core values and competency framework. 3. Craft Showcase: A chance to demonstrate your real-world expertise. Depending on the role, this will be a practical presentation. Your Talent Attraction Partner will be in your corner through every step right up to your Welcome Ritual on day one Our Benefits We believe in supporting our team, inside and outside of work. Here's a glimpse of what you can expect: Rest & Recharge: You'll receive 27 days of annual leave, plus 8 bank holidays, totaling 35 days of holiday to unwind and pursue your passions. Financial Future & Security: Plan for retirement with our company-matched 5% pension contribution. For added peace of mind, we also offer a Death in Service benefit, providing 2 x your annual salary for your loved ones. Health & Wellness: Our Medicash Health Cash Plans help cover everyday healthcare costs, complemented by an Employee Assistance Programme for confidential support, and Group Sickness Cover for peace of mind. Flexible Working: We champion hybrid working, providing a home office setup budget to ensure you're comfortable and productive wherever you are. Learning & Development: Invest in yourself with access to Udemy Business and a dedicated learning and development budget for continuous growth. Lifestyle Perks: Make the most of our Cycle to Work Scheme, and enjoy exclusive gym and retail discounts. Family First: Our enhanced parental leave policies include support for fertility journeys, recognising diverse family needs. Community & Connection: We foster a vibrant culture with regular fun meet-ups and lunch on us in the office once a month. You'll also have the opportunity to make a local impact through Spirit of Waracle, our initiative for getting involved in charitable causes locally, building strong team bonds and giving back. We are an equal opportunities employer and welcome applications from all suitably qualified persons regardless of their race, gender, disability, religion/belief, sexual orientation or age.

Spencer Rose are urgently seeking a Data Scientist for a Coach AI programme working for our banking client located in Bristol. 6-12 month contract, Immediate start Start: ASAP Location: Bristol - 2 days a week onsite Duration: 6-12 months Rate: (Apply online only)pd Inside IR35 The role: Design, develop, and deploy end-to-end AI/ML solutions - with Production support experience (Production Deployment experience) Build scalable and robust AI architecture, including LLM-powered systems and multi-agent frameworks Translate business requirements into actionable AI models and system designs Ensure performance, reliability, scalability, and cost optimization of AI solutions Develop and implement Generative AI solutions using Large Language Models (LLMs) Build Agentic AI systems, including multi-agent orchestration and autonomous workflows Design and implement LLM-based assistants, copilots, and conversational AI systems Integrate AI solutions with enterprise applications and APIs Hands on experience in Natural Language Processing (NLP), Computer Vision & Voice AI Build and manage ML pipelines (training, validation, deployment, monitoring) Ensure model explainability, fairness, and governance compliance Implement AI guardrails including safety, bias detection, and hallucination controls Ensure adherence to Responsible AI principles (security, privacy, compliance) Identify risks/issues and come up with solution/mitigation plans Collaboration with internal and stakeholders to ensure delivery as per plan with quality Grooming the internal Team, as senior Lead, for technical competence Production Experience-To ensure code development & deployment in production Core Technical Skills Strong experience in: Machine Learning & Deep Learning frameworks (TensorFlow, PyTorch, etc.) Generative AI & LLM ecosystems (OpenAI, Hugging Face, LangChain, etc.) Agentic AI/Multi-agent system design Expertise in: NLP, Computer Vision, and Conversational AI Model deployment (MLOps, APIs, cloud platforms) Data processing and feature engineering Expertise in source safing tools like Github/GitLab/Bitbuck Interviews are taking place next week, so if available and interested APPLY NOW

Senior Applied AI Engineer Contract Outside IR - 900 per day DOE 6 months + likely extension Oxfordshire (hybrid - 2 days p/w on site) Our client is a rapidly scaling UK deep-tech company delivering advanced agentic AI capability. This role sits in their AI team, working with a commercial customer in biotech and pharma. They are hiring a Senior Applied AI Engineer to take ownership of the agentic AI systems underpinning their platform, on a contract basis, with genuine influence over how those systems are architected and delivered. This role suits an engineer who designs and ships systems rather than assembling pre-built components, someone who cares more about how a system performs once it's live and under pressure than how impressive it looks in a demo. What You'll Be Doing Architecting and building multi-agent AI systems end-to-end, combining frameworks such as LangGraph or Haystack where they add value with bespoke orchestration, state management and tool-calling logic where a framework won't cut it Writing production-grade code with reliability, latency and graceful failure handling designed in from the start rather than bolted on afterwards Embedding LLMs and vision-language models into live reasoning, search, summarisation and task-execution workflows, backed by solid retrieval, memory and guardrail design Deploying across cloud and on-premises environments, adapting your approach to each environment's security and connectivity constraints Taking end-to-end ownership of production pipelines, from raw data ingestion through to live inference Building the monitoring, evaluation and guardrail tooling that shows how a system is actually performing in the field Setting technical standards for agentic AI within the team, establishing the patterns other engineers follow and leaving clear documentation behind What We're Looking For Robust software engineering, including developing LLM-powered or agentic systems, and a track record operating at senior/lead level Strong engineering fundamentals: code that's clean, tested and built to last, not just prototypes that work once Commercial track record building and owning multi-agent or agentic AI systems in live production settings, as an engineer rather than an integrator Strong Python skills, comfortable working with LangGraph, LangChain, Haystack or similar, and equally comfortable dropping below the framework when that's the right call Real-world experience deploying AI/ML systems into production, beyond notebooks and proof-of-concepts Confidence across inference performance, data pipeline design, state/memory handling, and tool or function calling Experience building retrieval and search over very large, multi-modal datasets, including indexing and embedding at scale Comfortable with Docker, Git and cloud infrastructure, ideally AWS Understanding of secure deployment approaches, including on-premises and sovereign cloud Nice to Have Biotech, pharma or life sciences domain experience Multimodal reasoning experience Kubernetes experience (EKS or OpenShift) MLOps background: evaluation, monitoring, reproducibility Observability for agentic systems, covering drift, agent behaviour and performance Familiarity with agent-to-agent orchestration protocols or the Model Context Protocol (MCP) Awareness of secure-by-design frameworks (ISO 27001, NIST, OWASP) Open-source AI/ML contributions Why Apply Real technical ownership: shape the architecture, not just work through a backlog Work at the leading edge of agentic and generative AI, on systems people genuinely depend on Join a fast-scaling business with a strong chance of the contract extending or evolving further Strong day rate reflecting seniority and scope This is a contract engagement operating outside IR35 , offered on an initial 6-month term with a strong likelihood of extension. Deerfoot Recruitment Solutions Ltd is a leading independent tech recruitment consultancy in the UK. For every CV sent to clients, we donate 1 to The Born Free Foundation. We are a Climate Action Workforce in partnership with Ecologi. If this role isn't right for you, explore our referral reward program with payouts at interview and placement milestones. Visit our website for details. Deerfoot Recruitment Solutions Ltd acts as an Employment Business in relation to this vacancy.

Location: Reading, United Kingdom Thales is a global technology leader with more than 83,000 employees on five continents. With over 7,500 people in the UK, operating across defence, space, aerospace, and digital security, we help build a future we can all trust. Thales supports the security and stability of our nation by providing extraordinary technology to our customers, as well as delivering social value to the UK with our products and services. AI Researcher Reading (RG2 6GF) - Hybrid Primary Purpose of the Role: To conduct high-impact AI research that develops novel, trustworthy and operationally relevant AI capabilities across Thales UK businesses and for our customers, increasing the quality of our offers, winning new business, strengthening technical differentiation, and improving customer outcomes. As part of the growing software, AI and research capability in cortAIx Factory / cortAIx UK, the AI Researcher will collaborate with AI engineers, AI assurance specialists, human-machine teaming researchers, product owners, domain experts, data engineers and software engineers to turn complex operational and business challenges into validated AI research outcomes, proofs of concept and transferable technical capability. The role will contribute novel AI methods, research assets, technical reports, publications, invention disclosures and reusable experimental approaches to Thales UK's internal catalogue of capabilities, accelerating responsible AI adoption across programmes. The role will connect with data, digital, research and engineering specialists across Thales UK and Group, maturing emerging AI technologies for future deployment and acting as a technical expert on advanced AI research used transversally throughout the business. We are interested in people with a background of both conduct research and delivering across a diverse range of products or industries. We are looking for those who can undertake the research but also understand how to translate this into product. Highly advantageous if you've supported or led bids/bid requirements. Key Responsibilities and Tasks: Conduct applied and experimental AI research to solve complex customer and business problems across defence, aerospace, cyber security, rail, critical national infrastructure and related domains. Develop state-of-the-art AI/ML solutions, proofs of concept and research prototypes using real-world data and operationally relevant problem statements. Investigate, design, implement and evaluate advanced AI methods, including but not limited to deep learning, multimodal AI, self-supervised learning, foundation models, generative AI, computer vision, NLP/LLMs, time-series analytics and reinforcement learning where relevant. Translate business and customer needs into clear research questions, experimental plans, technical requirements and measurable success criteria. Design robust evaluation methodologies, including baselines, benchmarks, ablation studies, uncertainty assessment, robustness testing and performance measurement against operationally meaningful metrics. Collaborate with AI V&V, AI Assurance, Human-Machine Teaming and Applied AI groups to ensure research outputs are trustworthy, human-centred, explainable and suitable for future operational use. Build reproducible research pipelines, including data preprocessing, feature engineering, model training, experiment tracking, evaluation and technical reporting. Ensure Responsible AI practices are embedded throughout the research lifecycle, including robustness, safety, explainability, transparency, fairness, privacy, security and alignment with MOD, regulatory and Thales governance requirements. Create proofs of concept, publications, invention disclosures, patents, technical reports and reusable research assets around advanced AI topics such as multimodal learning, self-supervised learning, foundation models and human-AI collaboration. Package research outputs in a form that enables transition to AI engineering teams, including demonstrator code, model cards, experiment reports, design notes and handover documentation. Support bids, PoCs, demos, customer workshops, innovation campaigns and stakeholder briefings by communicating research concepts and outcomes to technical and non-technical audiences. Work with data engineers, architects and domain experts on data acquisition, labelling strategies, synthetic data approaches, integration of third-party data and data quality management. Horizon scan for major AI research and technology trends, assess relevance to Thales markets, run trials and share best practices to accelerate responsible adoption. Skills, experience and qualifications required Degree/Masters, an equivalent in a relevant Software/AI subject, or equivalent experience. Relevant subject areas may include Artificial Intelligence, Machine Learning, Computer Science, Data Science, Mathematics, Engineering, Physics or a related technical discipline. Strong Python programming skills; proficiency with modern software engineering and research practices, including testing, code quality, reproducibility and collaborative development. Experience conducting AI/ML research or advanced AI development in complex technical environments, preferably including defence, aviation, rail, cyber security, safety-critical, mission-critical or similarly regulated domains. Expertise in ML/DL algorithms and techniques for supervised, unsupervised, self-supervised and, where relevant, reinforcement learning. Proven ability to take AI research from problem framing through experimental design, model development, evaluation and prototype demonstration. Hands-on experience in at least one advanced AI area such as deep neural networks, computer vision, NLP/LLMs, multimodal AI, self-supervised learning, reinforcement learning, time-series analytics or foundation models. Experience with AI frameworks and libraries: PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers; OpenCV for vision applications. Strong understanding of experimental design, statistical evaluation, benchmarking and model validation. Experiment tracking and reproducibility tools, for example MLflow, Weights & Biases or equivalent. Data wrangling and analysis using Pandas, NumPy, SQL; familiarity with Spark or similar is a plus. Model optimisation and research-to-prototype deployment fundamentals, including ONNX, TorchScript, FastAPI/gRPC and GPU acceleration, with CUDA basics desirable. Responsible AI and security awareness: explainability, privacy-preserving methods, bias assessment, safety, assurance, adversarial robustness and secure AI. Scientific and technical writing skills, including preparation of technical reports, research papers, invention disclosures, model cards and experiment reports. Demonstrable experience producing high-quality technical documentation, research outputs, model evaluations or stakeholder briefings. Interpersonal Skills Ability to engage and influence diverse stakeholders, including Product Engineering Leaders, Customers, Design Authorities, Project Management, IS/IT, research partners and academic collaborators. Highly effective in a matrix-based organisation; a collaborative team player who drives outcomes while maintaining scientific rigour. Excellent communication skills; able to explain complex AI research concepts clearly to technical and non-technical audiences. Curious, creative and intellectually rigorous, with the ability to challenge constructively and develop novel solutions to ambiguous problems. Encourages an open environment where ideas are shared, technical debate is welcomed and innovation thrives. Desirable PhD or equivalent research experience in Artificial Intelligence, Machine Learning, Computer Science, Mathematics, Engineering, Physics or a related discipline. Peer-reviewed publications, patents, invention disclosures, open-source contributions or recognised technical innovations. Experience with governance of architecture, research designs or detailed technical designs throughout the project lifecycle. Experience with large-scale data initiatives, data labelling strategies, synthetic data generation and data quality management. Familiarity with MLOps practices and cloud platforms for AI deployment. Experience working with academic partners, research institutions, grant-funded programmes or collaborative research consortia. Experience contributing to bids, proposals, customer demonstrations or externally funded innovation activities. Knowledge of cloud AI services, HPC environments, containerisation and secure research environments is desirable. Security Clearance statement Due the nature of the work that we do at Thales, many of our roles are subject to security restrictions. This role requires you to be a sole British National and achieve Security Clearance (SC) without any caveats. It would be advantageous if currently held, however, if not currently held, it is a requirement that the successful applicant undergo, achieve, and maintain SC Clearance prior to commencing employment. Please visit the UKSV website for further guidance- United Kingdom Security Vetting - GOV.UK ( ) To be eligible for full SC, you generally need to have resided in the UK for the last 5 years. In some circumstances . click apply for full job details

Machine Learning Engineer Location: London Salary: £95,000-120,000 Contract: Permanent Working pattern: On site, 3 days a week A new software venture built inside an established global provider of analytical and research services to the private capital industry is hiring Machine Learning Engineers to build the models, agents and evaluation systems that investment professionals rely on to analyse data, work with documents and automate research. You'll join the UK core team of around thirty people, working in Python on production ML and LLM systems that sit behind data-dense enterprise products. THE ROLE You'll report to the Head of Engineering and own the machine learning layer of the platform, from the first prototype through to a monitored production service. The work combines applied LLM engineering with the rigour that high-stakes financial workflows demand. The hard part is reliability and measurement: a model that is right most of the time is not enough when analysts act on the output, so every system needs to be evaluated, observable and safe to fail. Part of an engineering cohort being hired across machine learning, front-end and back-end. Based with the UK core team of around thirty people in Kings Cross, alongside colleagues in London and India. WHAT YOU'LL BE DOING Your remit spans the models and agents, the data and retrieval layer, the evaluation framework and the production platform. Design, build and ship LLM-powered features and multi-step agent workflows in Python, from prototype to production. Build document intelligence pipelines that extract, structure and reason over long, messy financial documents such as filings, decks, models and data rooms. Develop retrieval-augmented generation systems, including chunking, embeddings, vector search, re-ranking and citation, so that every answer can be traced to its source. Create evaluation frameworks and datasets, with automated regression tests, human review loops and metrics for accuracy, that catch mistakes early and stop the same failures recurring. Implement guardrails, confidence signalling and safe failure behaviour, and work with front-end colleagues to expose approvals, tool calls and partial progress to users. Fine-tune, prompt-engineer and benchmark models, and make considered decisions between hosted foundation models, open-weight models and classical ML on cost, latency and quality. Build and operate ML services and APIs with authentication, role-based access and tenant isolation, so client data is never mixed across tenants. Own MLOps in production: experiment tracking, model and prompt versioning, monitoring, drift detection, cost and latency management, and incident response. Write unit, integration and evaluation tests, and ship through Jenkins and SonarQube pipelines to AWS. Work with back-end and front-end engineers and with domain experts in private capital to turn real analyst workflows into reliable products. WHAT WE'RE LOOKING FOR 5+ years in machine learning or software engineering, with production ML experience and strong Python. Hands-on experience shipping LLM-based or NLP systems to real users, including RAG, agents and tool use, and the ability to explain the design and trade-off decisions behind them. A rigorous approach to evaluation: you can define what "good" means for a model, build the test set and measure it. Strong grounding in the ML stack, such as PyTorch, scikit-learn, Hugging Face and vector databases, alongside sound software engineering practice. Experience deploying and monitoring models on cloud infrastructure, ideally AWS, with Git, CI/CD and containerisation. Experience building SaaS or multi-tenant products, with a working understanding of data security, access control and privacy. Daily use of AI coding tools such as Cursor or Claude Code, and a considered view of where they help. A degree in computer science, engineering, mathematics, statistics or a related field, or equivalent experience. A postgraduate degree is valued but not required. NICE TO HAVE Experience building agentic products, including orchestration, memory, tool calling and human-in-the-loop controls. Document understanding, OCR and table extraction on complex financial documents. Fine-tuning or distillation of language models, and inference optimisation. Experience with financial, investment or other regulated-industry data. Experience with LLM observability and evaluation tooling. THE REALITY Autonomous systems are being built inside high-stakes live businesses, where decisions carry real consequences and waiting for everyone to agree is not always an option. The systems will sometimes get things wrong; in this phase the London team needs to create the evaluations, visibilities and safeguards that catch mistakes early and stop the same failures recurring. The goal is to build the foundations of an enterprise-grade technology platform serving a multi-billion dollar serviceable market, and to share in that growth. IN OUR CONVERSATIONS Come ready to discuss three pieces of work: A production ML or LLM system you shipped: the architecture, data and cost decisions. An evaluation approach you designed to measure or improve quality, and what it revealed. A failure or regression you found in production and the improvement you made to prevent it recurring. LOCATION & WORKING PATTERN Kings Cross, London. In office 3 days a week. Colleagues based in London and India. Visa sponsorship is not available for this role. The interview process is intended to complete within three weeks, and adjustments to interviews can be arranged on request. Oscar Associates (UK) Limited is acting as an Employment Agency in relation to this vacancy. To understand more about what we do with your data please review our privacy policy in the privacy section of the Oscar website.

Enterprise AI Solution Architect - West Midlands, hybrid working weekly on-site, Up to £90k, 6 Month initial FTC - Scope for extension and permanent Step into a pivotal role shaping an enterprise-scale AI future for a major organisation in the West Midlands. This fixed-term opportunity will see you architecting impactful AI solutions that transform internal business processes, reporting, frontline operations and customer experience, while setting the foundations for long-term innovation. You will work at the intersection of technology, strategy and commercial impact, translating ambitious ideas into secure, scalable AI capabilities. About the Role: You will be the Enterprise Scale AI Solution Architect responsible for designing and validating proof-of-concept solutions that underpin the organisation s AI roadmap. Working across business units, you will define target architectures, orchestrate AI components, and create reusable patterns that accelerate delivery of high-value use cases. Your work will directly influence how AI is embedded into the data, knowledge and process layers of the enterprise, ensuring solutions are robust, compliant and commercially viable. Key Responsibilities: Architect end-to-end AI solutions across data, knowledge and process layers, covering internal business processes, reporting, frontline operations and customer experience. Design and deliver proof-of-concept AI solutions that validate target architectures, use cases and reusable patterns at enterprise scale. Define and document AI solution blueprints, including integration patterns for APIs, microservices, orchestration and cloud-native components. Lead the technical design of MLOps and model deployment pipelines, ensuring reliable, secure and compliant operation of LLMs, agentic AI and generative AI services. Select and integrate appropriate AI frameworks and tools (e.g. LangChain, Semantic Kernel, Llama, vector-based search, MCP/Model Context Protocol) into the enterprise architecture. Design and implement RAG (Retrieval-Augmented Generation) architectures, including data ingestion, vector stores, and knowledge layer orchestration. Ensure AI solutions meet security, governance and compliance requirements, including data protection, access controls and auditability. Collaborate with stakeholders across IT, data, security and business functions to refine use cases, align solutions with commercial objectives, and transition PoCs towards production readiness. Key Requirements: Proven experience as an AI Solutions Architect or Enterprise Architect delivering AI/ML solutions at enterprise scale. Strong expertise in enterprise integration patterns, including APIs, microservices, event-driven architectures and cloud-native services. Experience with MLOps, model deployment and operationalising AI/ML models in production environments. Deep understanding of LLMs, generative AI, agentic AI and their application to real-world business use cases. Practical experience designing and implementing vector-based search and retrieval systems within AI solutions. Strong knowledge of security, compliance and governance in AI systems, including data privacy and responsible AI considerations. Ability to define target architectures, solution designs and reusable patterns that support scalability, maintainability and commercial impact. Proven track record of working with cross-functional teams to translate business requirements into technical AI solutions. Desirable Skills: Experience with LangChain, Semantic Kernel, Llama and related frameworks for building LLM-based applications. Familiarity with Model Context Protocol (MCP) and emerging standards for AI orchestration and context management. Experience designing and implementing RAG architectures in enterprise environments. Background in cloud platforms (e.g. AWS, Azure, GCP) and their AI/ML services. Exposure to knowledge management, knowledge graphs or advanced search systems integrated with AI solutions. Experience in designing AI solutions for customer experience, contact centres, or frontline operations. Previous involvement in building AI roadmaps or strategic AI initiatives within large organisations. Qualifications: Degree in Computer Science, Software Engineering, Data Science, Information Systems or a related technical discipline is ideal. If you are ready to shape an enterprise AI roadmap and deliver high-impact solutions in a fixed-term role, we welcome your application.

Waracle are looking for a Lead Devops Engineer for an exciting new role joining our world-class digital technology consultancy and home to a diverse, smart, curious and ambitious community of specialists in technology-driven transformation. We work with ambitious clients to help them solve their biggest business and customer challenges. We help our clients to innovate and create intelligent digital products and services. We thrive on complex challenges and deliver business-critical IT transformation projects, moving seamlessly from strategy, design and delivery to operations. This is a Hybrid role working from our Glasgow, Dundee or London office 2 days a week. As a Lead DevOps Engineer , you'll serve as an empathetic leader, strategic advisor, and technical authority across our engineering squads. Bringing a broad, generalist mindset across platform engineering and modern infrastructure, you will lead the integration of AI-driven delivery practices, intelligent automation, and Generative AI tooling into everyday workflows. You will define project-level technical architecture, mentor and lift up growing engineers, and nurture a collaborative, psychologically safe environment where everyone feels empowered to innovate. Balancing short-term delivery with long-term maintainability, you will play a key role in de-escalating conflicts, aligning client stakeholders, and driving high-impact technical strategy aligned with human-centric business goals. Key aspects of the role include Technical Strategy & AI Integration: Defining technical architecture across squads, anticipating system scaling risks, and driving the adoption of modern AI tooling, MLOps, and intelligent automation within cloud environments. Broad Generalist Leadership: Leveraging your breadth of engineering knowledge to bridge gaps between software delivery, platform operations, and data infrastructure, making complex trade-offs clear and explicit for clients. People & Culture: Creating an inclusive, psychologically safe team environment, regularly coaching team members with constructive feedback, and balancing delivery pace with overall team wellbeing. Standards & Compliance: Guiding teams on performance strategies, proactive optimisation, and security standards (such as OWASP, GDPR, SOC2), ensuring responsible AI practices, clean architecture, and secure code execution. Stakeholder Collaboration: Facilitating workshops to discover requirements, managing project scope and expectations, and translating client priorities into actionable technical strategy. Innovation & Influence: Evaluating emerging AI tools and technical approaches through tech spikes, shaping go-to-market strategies for new offerings, driving delivery tooling improvements, and representing Waracle at external tech events. Research shows that underrepresented groups tend to only apply for roles that they believe they meet 100% of the criteria for. At Waracle, we want to buck that trend! We hire for passion, potential and mindset as much as credentials. If you think you'd be incredible in the role, then apply - even if you don't think you tick every box. Job requirements What you'll bring A versatile, generalist engineering background with broad knowledge across cloud platforms, CI/CD, containerisation, and infrastructure-as-code. Practical exposure to or enthusiasm for integrating Generative AI, AI-assisted development tools, or modern MLOps pipelines into delivery streams. Proven experience guiding technical direction and architecture for cloud and platform solutions. A passion for mentorship, coaching, and creating supportive pathways for engineers at all career stages. Commitment to fostering inclusive, collaborative team dynamics that prioritise wellbeing alongside quality outcomes. Strong understanding of security, accessibility, and compliance standards (OWASP, GDPR, SOC2) in modern cloud architectures. Excellent communication skills with an aptitude for breaking down complex technical concepts for diverse stakeholders. Familiarity with tracking key engineering metrics (such as DORA) to guide continuous, data-informed improvement. What to expect from our hiring process 1. First Hello: An informal chat with your dedicated Talent Attraction Partner to talk about Waracle, what you want out of your next move, salary, benefits, and office hybrid working model. 2. Talking Impact: A collaborative deep dive into how you solve problems, work with others, and raise the bar, guided by our core values and competency framework. 3. Craft Showcase: A chance to demonstrate your real-world expertise. Depending on the role, this will be a practical presentation. Your Talent Attraction Partner will be in your corner through every step right up to your Welcome Ritual on day one Our Benefits We believe in supporting our team, inside and outside of work. Here's a glimpse of what you can expect: Rest & Recharge: You'll receive 27 days of annual leave, plus 8 bank holidays, totaling 35 days of holiday to unwind and pursue your passions. Financial Future & Security: Plan for retirement with our company-matched 5% pension contribution . For added peace of mind, we also offer a Death in Service benefit, providing 2 x your annual salary for your loved ones. Health & Wellness: Our Medicash Health Cash Plans help cover everyday healthcare costs, complemented by an Employee Assistance Programme for confidential support, and Group Sickness Cover for peace of mind. Flexible Working: We champion hybrid working , providing a home office setup budget to ensure you're comfortable and productive wherever you are. Learning & Development: Invest in yourself with access to Udemy Business and a dedicated learning and development budget for continuous growth. Lifestyle Perks: Make the most of our Cycle to Work Scheme , and enjoy exclusive gym and retail discounts . Family First: Our enhanced parental leave policies include support for fertility journeys, recognising diverse family needs. Community & Connection: We foster a vibrant culture with regular fun meet-ups and lunch on us in the office once a month. You'll also have the opportunity to make a local impact through Spirit of Waracle, our initiative for getting involved in charitable causes locally , building strong team bonds and giving back. We are an equal opportunities employer and welcome applications from all suitably qualified persons regardless of their race, gender, disability, religion/belief, sexual orientation or age.

Senior Learning Consultant - AI & ML Engineering (Remote working) We're working with a leading technical training & enablement organisation looking to hire Senior Learning Consultants across AI & ML Engineering . They operate at the forefront of AI, ML & Data - training AI/ML Engineers, Data Scientists & technical leaders within some of the world's leading technology organisations . You'll work across areas that align with your own technical experience , so you don't need to be an expert across the entire AI/ML landscape, and you'll receive ongoing upskilling in the latest AI technologies & wider AI portfolio. The role will involve a mix of: Delivering advanced technical AI & ML training to Engineers, Data Scientists & other technical professionals Designing and developing curriculum & learning solutions Working with enterprise clients to understand their technical learning needs Developing your own technical knowledge across emerging AI/ML technologies What we're looking for: Strong hands-on experience with Python and modern AI/ML technologies - this could include areas such as Machine Learning, Deep Learning, Generative AI/LLMs or MLOps . Previous training, teaching or mentoring experience would be valuable, but we're also interested in experienced AI/ML professionals who have the communication skills and interest to move into a training/consulting-focused role . You DO NOT need to have worked as an official Trainer / Instructor / Lecturer / Mentor / Coach etc before. This is a permanent, remote working role. If you're an experienced AI/ML professional who enjoys sharing technical knowledge and wants to work with some of the world's leading technology organisations, please reach out for more info!

Are you an early-career Data Scientist who enjoys solving real-world problems with messy data, building practical machine learning solutions and turning complex information into actionable business insights? Our client is an ambitious, PE backed business based in Windsor, at the beginning of their journey to become a data enabled company with the adoption of advanced analytics, AI and ML operations. We're looking for a hands-on, numerically strong data professional to join a growing business and help transform its data capabilities. This is an opportunity to work across data science, engineering, AI and visualisation, with plenty of scope to develop your skills and make a tangible impact. This is an ideal opportunity for someone who wants to get hands-on, take ownership and see their work make a genuine difference to business performance. The Role Clean, reconcile and integrate messy data from multiple systems using SQL, data pipelines and fuzzy matching techniques. Develop, test and improve machine learning models, including predictive and risk modelling. Explore AI applications to extract and validate information from documents, identify reliable data sources and automate processes. Support the integration of data and ML models within a Snowflake environment. Build data visualisations and dashboards to communicate insights to business leaders and investors. Work with stakeholders to translate business challenges into practical, data-driven solutions. What we're looking for A degree or postgraduate qualification in Data Science, Statistics, Mathematics, Computer Science or a related quantitative discipline. Some practical experience with machine learning, AI or predictive modelling, gained through university projects or professional experience. Strong SQL skills and an understanding of data structures, data integration and statistical methods. A practical, proactive approach and the confidence to tackle imperfect data and ambiguous problems. Strong communication skills, with the ability to explain findings and recommendations to non-technical stakeholders. Experience with Power BI, Snowflake, Python, MLOps, Docker, CI/CD or AI-powered applications would be advantageous. Why join? Salary from £50,000, with flexibility for the right candidate. 15% bonus potential, plus a 10% car allowance or pension contribution. Hybrid working, typically 3-4 days per week in Windsor. The opportunity to work with emerging AI technologies and help shape a growing data function. Clear scope for career progression as the business expands. Robert Half Ltd acts as an employment business for temporary positions and an employment agency for permanent positions. Robert Half is committed to diversity, equity and inclusion. Suitable candidates with equivalent qualifications and more or less experience can apply. Rates of pay and salary ranges are dependent upon your experience, qualifications and training. If you wish to apply, please read our Privacy Notice describing how we may process, disclose and store your personal data:

Job Description This role requires once in a week travel to Newcastle. About Scrumconnect Consulting Scrumconnect Consulting is a multi-award-winning digital consultancy, recognised for delivering impactful and innovative technology solutions across UK government departments. Our work has positively influenced the lives of over 40 million UK citizens. We are passionate about user-centred design, agile delivery, and building digital services that make a real difference - and we're now scaling that expertise into large, high-stakes AI adoption programmes across the public sector. Overview We're looking for a Lead AI Engineer to be the hands-on technical builder at the core of a large-scale AI Operating Model programme for a central government department. Where the Lead Technical Architect sets direction, you turn it into working, production-grade systems - semantic search, RAG pipelines, and broader generative AI capability - integrated into complex Legacy and multi-cloud environments handling high-volume, sensitive public sector data. You'll work inside a collaborative "Rainbow Team" alongside civil servants and the wider delivery team, staying close to the code while also mentoring engineers and helping build the internal capability the department needs to eventually run these systems without long-term reliance on external suppliers. Key Responsibilities Hands-On Build & Delivery Design, build, and ship production AI components - RAG pipelines, retrieval and embedding infrastructure, orchestration logic, and integration layers - writing high-quality, tested, maintainable code and staying close to implementation rather than delegating it away. Technical Leadership Within the Squad Lead the engineering practice within your delivery team: set coding standards, review designs and pull requests, unblock technically complex problems, and mentor other engineers day to day. Responsible AI in Practice Implement the guardrails the Architect designs - bias mitigation checks, evaluation harnesses, human-in-the-loop review points - so that Responsible AI principles (ATRS alignment, NCSC "Secure by Design, " meaningful human control) are enforced in the running system, not just on paper. Reliability, Security & Observability Build AI services to be secure-by-default and observable in production: logging, monitoring, alerting, and rollback paths appropriate for sensitive public-sector data and high-availability requirements. Knowledge Transfer & Capability Uplift Work within the OKUA (Ownership, Knowledge, Understanding, Awareness) framework and "Docs-as-Code" practices to pair with and upskill internal government engineers, so capability genuinely transfers rather than staying locked in the consultancy team. Efficient, Sustainable Engineering Favour low-modality, resource-efficient designs where they meet the need - right-sizing models and infrastructure rather than defaulting to the largest/most expensive option - in line with the programme's Green AI and Net Zero commitments. Required Skills & Experience Loosely mapped to the Government Digital and Data (DDaT) framework, at Lead Engineer level: Coding and Scripting (Expert) - writing production-grade, well-tested code; setting standards for others; comfortable owning components end-to-end. Systems Design (Practitioner) - designing components that integrate cleanly into a wider, architect-defined system; understanding trade-offs across the stack. Data Engineering (Practitioner) - building reliable pipelines to ingest, clean, and prepare data (including unstructured/Legacy sources) for AI consumption. DevOps/Continuous Delivery (Practitioner) - CI/CD pipelines, infrastructure-as-code, and automated deployment for AI workloads specifically (not just conventional web services). Testing & Evaluation (Practitioner) - beyond conventional unit/integration testing, building evaluation harnesses for AI system quality: retrieval accuracy, hallucination rate, bias/fairness checks. Problem Solving (Practitioner) - diagnosing and resolving complex, ambiguous technical issues under production pressure. Agile Working (Practitioner) - delivering iteratively within a blended, multidisciplinary team including civil servants. Given the scale of this programme, direct hands-on depth across the AI engineering stack is essential: Strong general-purpose programming (most commonly Python) applied to AI/ML systems Semantic search, vector/embedding infrastructure, and RAG pipeline construction LLM orchestration and agentic frameworks (eg LangChain/LlamaIndex-style tooling, multi-agent patterns) Prompt engineering and systematic evaluation/guardrail tooling (hallucination detection, safety testing) LLMOps/MLOps - model versioning, deployment, monitoring, and rollback for AI services in production Cloud-native engineering across major providers (AWS, Azure, or GCP), including their AI/ML tooling Containerisation and infrastructure-as-code for repeatable, auditable deployments Secure-by-design engineering appropriate to sensitive public-sector data Desirable Experience AWS/Azure/GCP certifications (associate or professional level) Prior delivery of AI or digital services within UK central government or wider public sector Experience fine-tuning or adapting open-source/foundation models for a specific domain Open-source contributions or active engagement in AI/ML engineering communities Experience designing for sustainability/Green IT commitments The technological capabilities can span a wide range of AI and automation tools, including Virtual Assistants, Robotic Process Automation (RPA), Computer Vision, Natural Language Processing (NLP), Machine Learning, Deep Learning, Generative AI, Frontier LLMs, and Predictive AI. A strong candidate would have experience/exposure to nuances of these tools/technologies. Location and Working Pattern Hybrid, based from Newcastle upon Tyne, with on-site attendance required for workshops, knowledge-transfer sessions, and onboarding. All production system and data access must be performed solely from within the UK. How We Work Our values: Collaboration - we are stronger as a team than as individuals; we tackle problems and celebrate wins together. Create Value Early - we find the quickest route from idea to product, because our clients rely on us to do what's best. Integrity - teamwork requires trust; we can always be relied upon to uphold the highest standards. Commitment - no matter what, no matter how, we give our 100% to keeping our promises and achieving our goals. Diversity and Inclusion - we believe diversity drives innovation and better outcomes, and we're committed to an inclusive environment where every individual is valued, respected, and supported. We welcome applications from candidates of all backgrounds, including women, people with disabilities, and diverse communities, as well as those seeking flexible working arrangements. As a Disability Confident Level 1 employer, we provide reasonable adjustments throughout recruitment and employment to ensure equal opportunity for all. How to apply Please submit your CV. Shortlisted candidates will be invited to interview. This role requires BPSS (Baseline Personnel Security Standard) clearance - please flag in your application if you already hold clearance.

About Ricoh A global leader in digital services, recognised for innovation, sustainability and a people-first culture. We feature in the Gartner Magic Quadrant , are listed in the Global 100 Most Sustainable Companies , and have been named one of Forbes' World's Best Employers 2025 . At Ricoh, we believe people do their best work when they feel valued and supported. We create inclusive workplaces where you can grow, contribute, and make a positive impact while helping to build a more sustainable future. Find your place. Transform your future Our purpose is centred on understanding and improving how people work. By focusing on real working experiences, we support individuals to develop their skills, realise their potential and do work that feels meaningful. People transform when they Love What They Do This belief sits at the heart of The Ricoh Promise. It guides how we recruit, how we support our people, and how we work together every day, creating an environment where you can grow, feel valued and make a difference. When you join us, you are encouraged to share your ideas, challenge the way things are done, and work with others to build something better. If you are looking for a place where your voice is heard, your development is supported, and your work feels meaningful, you will feel at home at Ricoh. Are you a DevOps or Cloud Engineer looking to take your experience into the rapidly evolving world of Enterprise AI? We're looking for a DevOps Engineer - Enterprise AI to help build, configure and operate the platforms and services that enable AI solutions to move from evaluation into secure, scalable and reliable production use. Working as part of our Enterprise AI team, you'll sit at the intersection of DevOps, cloud engineering, AI and MLOps , helping us create the technical foundations that allow AI solutions to be deployed effectively across the organisation. This is a great opportunity for someone with a strong DevOps, cloud, platform or service operations background who wants to develop their expertise within AI and help shape how enterprise AI services are delivered. What you will be doing As our DevOps Engineer for Enterprise AI, you'll work closely with AI specialists, data scientists, software engineers, architects, cyber security teams and technology partners to configure, integrate and operate our AI platforms and services. You'll help establish reusable environments, integrations and deployment processes while ensuring our AI services remain secure, resilient, observable and cost-effective. The role covers both managed and off-the-shelf AI capabilities across cloud and hybrid environments, giving you the opportunity to work with a broad range of technologies while developing expertise in AI platforms, MLOps and responsible AI delivery . What you will be doing Configuring and maintaining secure, scalable environments and services for AI workloads. Building repeatable deployment processes for applications, AI services, models, prompts and configuration. Supporting the integration of cloud, AI and automation services with enterprise systems. Applying DevOps and MLOps principles across testing, release management and controlled deployment. Using automation and scripting to improve deployment, integration and operational processes. Monitoring service performance, availability, reliability, quality, usage and cost. Implementing appropriate identity, access, secrets management and security controls. Supporting incident resolution, troubleshooting and root-cause analysis. Helping establish monitoring, alerting, backup, recovery and service operating procedures. Optimising cloud consumption, platform subscriptions, licensing and capacity. Creating reusable templates, documentation and self-service capabilities for technical teams. Working with security, privacy, risk and compliance colleagues to support responsible and governed use of AI. Collaborating with technology providers and delivery partners to continuously improve our platforms and services. You will ideally have We're looking for someone who brings a strong foundation in DevOps, cloud engineering, platform administration, automation, service operations or MLOps , with an interest in applying those skills to Enterprise AI. Ideally, you'll have: Good knowledge of DevOps principles including source control, testing, release management and deployment processes. Hands-on experience configuring or administering a major cloud or AI platform. Experience working with cloud services including identity, networking, storage, access and managed services. Experience configuring managed or packaged technology services and integrating them with enterprise systems. Knowledge of CI/CD, configuration management and controlled promotion between environments. Scripting or automation experience using Python, shell or equivalent technologies . An understanding of APIs, secrets management, vulnerability management and secure configuration. Experience monitoring production services using logs, metrics and operational data. Strong troubleshooting and root-cause analysis skills. Experience working within multidisciplinary agile, product or engineering teams. Strong communication skills with the ability to explain technical issues, risks and recommendations clearly. Experience with containers, infrastructure as code, MLOps, generative AI services or AI platforms would be particularly beneficial, alongside an understanding of data protection, responsible AI and model governance. A relevant degree or equivalent practical experience is welcomed, while cloud, DevOps, security, service management or platform certifications would be advantageous. In return for your commitment, you can expect At Ricoh, work should feel meaningful, supportive and fulfilling. The Ricoh Promise shapes your experience through four pillars that bring our culture to life. Love to Connect You become part of a global community built on openness, inclusion and genuine collaboration. Across teams, countries and roles, you'll find people who listen, involve and encourage you - helping you feel valued and able to be yourself every day. Love to Grow Your development truly matters to us. With access to learning pathways, mentoring and career opportunities across functions and countries, you'll be supported to stretch your skills, explore new directions and stay future-ready in a changing world. Love to Give Back Purpose is part of how we work. You'll have opportunities to make a difference through volunteering, sustainability initiatives and community programmes that reflect our shared values and commitment to positive impact. Love to Succeed Success at Ricoh is something we pursue together. You'll benefit from fair rewards, flexible working, wellbeing resources and real recognition - including programmes such as the Imagine. Change. Awards, where colleagues celebrate each other's achievements. We are an equal opportunities employer We believe that diverse perspectives make us stronger, and we welcome applications from people of all backgrounds, identities, and experiences. Our hiring decisions are based on skills, experience and potential, and we are committed to creating a fair and inclusive recruitment process. If you require any reasonable adjustments at any stage of the recruitment journey, please let us know and we will support you to bring your best self forward. Ready to love what you do? Apply now and help us shape what comes next.

Head of AI Engineering & Delivery - Agentic AI, Artificial Intelligence, Degree Location: Cambridge (Hybrid) Salary: Competitive + Bonus + Share Options About the Company This is an opportunity to join a pioneering artificial intelligence company operating within a niche yet highly lucrative industry. The business is a recognised leader in its field, delivering innovative solutions to complex real-world challenges. Backed by an experienced leadership team and exceptional AI, data science and engineering talent, the company is entering an exciting phase of growth and is seeking a Head of AI Engineering & Delivery to scale its AI capabilities, engineering function and delivery excellence. The Role Reporting to the executive leadership team, you will lead the development and delivery of advanced AI products and platforms, driving both technical strategy and execution. This is a key leadership role requiring a strong engineering background, proven leadership capability and the ability to deliver innovative AI solutions at scale. About You 10+ years' experience in Software Engineering, Data Science, Machine Learning Engineering or a related technical discipline. 5+ years leading high-performing engineering teams. Experience delivering enterprise software, AI or SaaS solutions into production environments. Exposure to Agentic AI, AI agents, intelligent automation or Generative AI technologies. Strong understanding of scalable software architecture, cloud platforms and engineering best practice. Proven ability to drive execution, delivery excellence and commercial outcomes. Experience operating within a high-growth technology environment. Technical Experience Experience across several of the following areas would be advantageous: Agentic AI AI Agents & Autonomous Workflows Generative AI & LLMs Retrieval Augmented Generation (RAG) Multi-Agent Systems MLOps / LLMOps LangGraph, LangChain, Semantic Kernel, CrewAI or AutoGen OpenAI Agents, Microsoft Copilot Studio and MCP Azure, AWS or GCP Education 2:1 degree or above from a Russell Group university in Computer Science, Artificial Intelligence, Mathematics, Physics, Engineering or a related STEM discipline. Key Responsibilities Define and execute the AI engineering strategy. Lead and develop software, platform and AI engineering teams. Deliver AI-enabled products and customer solutions. Drive adoption of Agentic AI and modern AI architectures. Build scalable, resilient and secure AI platforms. Establish engineering standards, governance and best practice. Foster a culture of accountability, execution and delivery excellence. Act as a trusted adviser to executive stakeholders and customers. Recruitment Statement Adecco is acting as an Employment Agency in relation to this vacancy. We are on the client's approved supplier list for this role.

Purpose: Lead the discovery and pilot migration while actively contributing to architecture, engineering, and delivery activities. Key Responsibilities Perform hands-on assessment of Azure ML models, pipelines, dependencies, and operational processes. Review existing Global AI Platform capabilities and identify required enhancements. Define migration patterns, standards, and technical approach. Lead delivery of the pilot migration for two selected models. Coordinate activities across Azure ML, Databricks, and platform engineering resources. Produce recommendations, migration roadmap, and reusable assets. Key Experience Azure ML and enterprise MLOps platforms. ML platform migration experience. Strong engineering background with hands-on delivery experience. Details 6 month inital contract Hybrid 3 days a week in London Inside IR35