5 jobs found

Principal AI Scientist - Learning & Assessment AI Location: UK Remote (Candidates must be UK based) Salary: 110,000 - 125,000 Employment Type: Permanent The Opportunity We're working with an innovative technology-led organisation that is transforming how AI is used within learning, assessment, and workforce skills development. They are seeking a Principal AI Scientist to lead the scientific design, validation, and evolution of next-generation AI-powered assessment and learning solutions embedded directly into the flow of work. This is a senior individual contributor role suited to someone with deep expertise across AI/ML, psychometrics, measurement science, and production-scale intelligent systems. The successful candidate will operate as a scientific authority within the organisation, shaping methodology, influencing product direction, and ensuring AI-driven solutions are fair, explainable, scalable, and scientifically rigorous. The role offers the opportunity to work at the intersection of modern AI systems, applied research, and enterprise-scale product delivery. The Role As Principal AI Scientist, you will take ownership of the scientific and methodological direction of AI-enabled learning and assessment products. You will work closely with product leadership, engineering teams, data scientists, and domain experts to translate advanced research into production-ready systems that deliver measurable user and business outcomes. This position requires someone comfortable operating in highly complex environments with significant technical, ethical, and commercial considerations. Key Responsibilities Scientific Leadership & Product Ownership Lead the scientific strategy for AI-enabled assessment, learning, and skills products Define robust methodologies balancing innovation, scalability, fairness, validity, and explainability Act as the senior scientific authority for assessment and measurement decisions Establish scientific standards, reusable frameworks, and evaluation methodologies Lead validation studies to ensure reliability, consistency, fairness, and performance stability Define and monitor scientific KPIs including drift detection, bias indicators, and model effectiveness Identify and mitigate scientific and measurement risks associated with AI systems at scale AI, Machine Learning & Modern AI Systems Apply AI and machine learning techniques, including LLMs and foundation models, to learning and assessment use cases Support development of AI-powered capabilities such as: Skills inference Adaptive assessment Automated content generation AI-driven feedback and reasoning systems Contribute to the evaluation and governance of agentic AI workflows Partner with engineering teams to ensure scientific integrity is maintained throughout implementation and deployment Support lifecycle monitoring and continuous improvement of production AI systems Research, Innovation & Strategic Influence Translate research findings into scalable, commercially viable product capabilities Produce thought leadership content including whitepapers and scientific insights Influence product and AI strategy through scientific expertise and evidence-based recommendations Support internal and external discussions around responsible AI and ethical AI implementation Engage with enterprise stakeholders and senior leadership on scientific and AI-related topics Required Experience Advanced degree (MSc or PhD preferred) in: Psychometrics Educational Measurement Statistics Machine Learning Artificial Intelligence Data Science Or equivalent commercial experience Proven experience building or leading AI-enabled assessment or measurement systems Deep expertise in validation methodologies, statistical modelling, and measurement theory Strong applied experience with machine learning and AI systems in production environments Practical understanding of modern AI architectures including Large Language Models (LLMs) Experience collaborating closely with engineering and product teams Ability to influence technical and strategic decisions through expertise rather than direct authority Experience operating in complex, high-ambiguity environments with significant business or ethical risk considerations Desirable Experience Experience applying LLMs within learning, workforce skills, or assessment products Exposure to adaptive testing, continuous assessment, or automated item/content generation Experience evaluating or governing AI agentic workflows Background within learning technology, education technology, workforce development, or skills ecosystems Experience working on enterprise-scale or long-lived AI platforms What They're Looking For A scientifically rigorous thinker with strong commercial awareness Someone passionate about responsible and explainable AI Comfortable balancing research innovation with practical delivery Strong communicator able to work across technical and non-technical audiences A collaborative leader who enjoys solving complex real-world problems Package 110,000 - 125,000 salary Fully remote role within the UK High-impact position within a cutting-edge AI environment Opportunity to influence next-generation AI products at scale Please note: Applicants must be based in the UK. Sponsorship is not available for this position.

We're hiring an experienced AI Platform Engineer to design, build and operate a production-grade Generative AI platform powering next-generation intelligent products. This is a hands-on engineering role focused on taking AI solutions from prototype to scalable, reliable services used in real-world environments. You'll sit at the intersection of DevOps, cloud infrastructure and applied AI owning the full lifecycle of Retrieval-Augmented Generation (RAG) and LLM-powered systems across modern cloud architecture. This role is about engineering, not research. You will architect and run the infrastructure that enables AI to perform securely, reliably and at scale ensuring performance, cost control and operational maturity as adoption grows. You'll work closely with AI engineers, security teams, and product stakeholders to transform experimental models into hardened, production-ready services while shaping a reusable AI platform capable of supporting multiple products. What You'll Be Doing Design and optimise scalable RAG pipelines and vector search systems Orchestrate multi-model AI services with a focus on latency, resilience and performance Productionise GenAI workflows and ensure they operate reliably under real usage Build and run AI services across AWS and Databricks Develop ingestion, embedding and retrieval pipelines Deploy containerised workloads via Kubernetes and Helm Implement Infrastructure-as-Code using Terraform Introduce end-to-end monitoring, tracing and alerting for AI workloads Improve inference and retrieval performance while reducing operational cost Establish fault-tolerant, scalable infrastructure patterns Embed security, evaluation and governance into the AI lifecycle Build CI/CD pipelines and automation to support continuous model deployment Create reusable platform components to accelerate future AI initiatives Strong experience in: Cloud infrastructure engineering (AWS-focused environments) Kubernetes, containerisation, and distributed systems Terraform / Infrastructure-as-Code CI/CD, automation, and platform reliability Running production workloads with high availability requirements Plus, experience with one or more of the following: MLOps or ML platform engineering RAG architectures, embeddings, or vector search Model serving, observability or performance optimisation Data / AI workflow orchestration in Databricks or similar ecosystems Why Join? Work on real-world AI systems operating at scale Own platform design decisions and influence long-term architecture Blend modern DevOps practices with cutting-edge Generative AI use cases Be part of a growing, innovation-driven engineering environment Opportunity to define how AI is operationalised across multiple products If you're excited by building the infrastructure that makes AI usable, scalable and reliable in production, we'd love to hear from you. 49914MSR3 INDLON The Portfolio Group are acting on behalf of our client in recruiting for this position.

AI Engineer - Defence RAG Systems ( Security Clearance Essential ) On Site 2 X Days a week, Plymouth Clearance: Active SC Essential Sector: Defence Role Overview Defence client requires an SC Cleared AI Engineer to build fully on-premises RAG systems using open-source technologies. You'll develop classified AI capabilities on air-gapped infrastructure with zero external dependencies. Key Responsibilities - Build end-to-end RAG pipelines on isolated defence networks using open-source LLMs (Llama 3, Mistral, Qwen) - Deploy local vector stores (Chroma, FAISS, Milvus) with sensitive document ingestion pipelines - Host and optimise LLMs using vLLM/TGI on local GPU clusters without internet connectivity - Implement agent orchestration using LangChain/LangGraph in completely offline environments - Design secure document processing for classified materials with appropriate data sanitisation - Build monitoring and evaluation systems that operate within air-gapped infrastructure Essential Requirements - Active SC Clearance (non-negotiable) - willingness to undergo DV if required - Demonstrable experience deploying open-source LLMs (Llama, Mistral, Falcon) on-premises - Expertise with local vector databases (Chroma, FAISS, Weaviate) in offline deployments - Strong vLLM/Text Generation Inference experience for high-throughput model serving - Proven ability to work on air-gapped systems with no external package repositories - Experience with GPU orchestration (NVIDIA A100/H100) and CUDA optimisation - Python expertise with offline dependency management and local package mirrors Technical Stack (All On-Premises) Models: Llama 3, Mistral, Qwen (locally hosted) Vector Stores: Chroma, FAISS, Milvus Orchestration: LangChain, LangGraph for agents Hosting: vLLM, TGI, Ollama on bare metal/private cloud Infrastructure: Air-gapped Kubernetes, local container registries Desirable Skills - Experience with defence/government IT security protocols - Knowledge of CIS benchmarks and NCSC guidelines - Familiarity with cross-domain solutions and data diodes - Understanding of classification marking and handling procedures

Your New Company and Role Join a dynamic digital team focused on delivering intelligent automation solutions using cutting-edge technologies. In this role, you'll help build and evolve a production-grade automation service that applies AI/ML to process high-volume, low-complexity workflows. You'll work on a modern AWS serverless platform, developing components for document analysis, signature detection, and predictive modelling. Collaboration with cross-functional teams is key to ensuring seamless integration with existing digital services. What You'll Need to Succeed Commercial experience with AI/ML technology: OCR, Object Detection and LLM analysis implementation Machine Learning & AI Libraries including: o Transformers/Hugging Face for working with pre-trained LLMs, fine-tuning, and inference o PyTorch for deep learning model development and training o OpenCV for computer vision tasks and image preprocessing in object detection o PIL/Pillow for image manipulation and format conversion o YOLO object detection frameworks Core Python Skills : Proficiency in Python 3.9+ with understanding of object-oriented programming, decorators, context managers, and async/await patterns Data structures and algorithms for efficient data processing and model optimisation Error handling and debugging using try-catch blocks, logging, and debugging tools Data Processing: Pandas and NumPy for data manipulation, cleaning, and numerical operations SQLAlchemy or psycopg2 for database connectivity and ORM operations Boto3 for AWS service integration and automation AWS (working within Technical Lead's architecture): Lambda function development with proper event handling and response formatting S3 operations including multipart uploads, presigned URLs, and event notifications CloudWatch logging and metrics for monitoring and debugging Understanding of IAM and security for role-based access and credential management Experience with CDK for infrastructure deployment SQS for message queuing EKS/ECS/Kubernetes for containerised AI deployments API Development : FastAPI for building REST APIs and model serving endpoints Requests library for HTTP client operations and external API integration Authentication/authorisation implementation (JWT, OAuth) Software Development: Making excellent quality AI/ML software collaboratively with other engineers Working effectively under technical leadership while contributing specialised AI/ML expertise Design and implementation of AI/ML solutions using service-based and serverless architecture Using written, verbal, and visual communication to explain AI/ML concepts to both technical and non-technical audiences Development Practices: Cloud monitoring, telemetry, intelligence tools for AI/ML systems, including Grafana Experience working in Agile delivery models - Scrum and/or Kanban frameworks Formal XP engineering techniques including TDD and pair programming Working within defined infrastructure-as-code frameworks What you need to do now If you're interested in this role, click 'apply now' to forward an up-to-date copy of your CV, or call us now. If this job isn't quite right for you, but you are looking for a new position, please contact us for a confidential discussion about your career. Hays Specialist Recruitment Limited acts as an employment agency for permanent recruitment and employment business for the supply of temporary workers. By applying for this job you accept the T&C's, Privacy Policy and Disclaimers which can be found at (url removed)

AI Engineer - Defence RAG Systems ( Security Clearance Essential ) On Site 2 X Days a week Plymouth Clearance: Active SC Essential Sector: Defence Role Overview Defence client requires an SC Cleared AI Engineer to build fully on-premises RAG systems using open-source technologies. You'll develop classified AI capabilities on air-gapped infrastructure with zero external dependencies. Key Responsibilities - Build end-to-end RAG pipelines on isolated defence networks using open-source LLMs (Llama 3, Mistral, Qwen) - Deploy local vector stores (Chroma, FAISS, Milvus) with sensitive document ingestion pipelines - Host and optimise LLMs using vLLM/TGI on local GPU clusters without internet connectivity - Implement agent orchestration using LangChain/LangGraph in completely offline environments - Design secure document processing for classified materials with appropriate data sanitisation - Build monitoring and evaluation systems that operate within air-gapped infrastructure Essential Requirements - Active SC Clearance (non-negotiable) - willingness to undergo DV if required - Demonstrable experience deploying open-source LLMs (Llama, Mistral, Falcon) on-premises - Expertise with local vector databases (Chroma, FAISS, Weaviate) in offline deployments - Strong vLLM/Text Generation Inference experience for high-throughput model serving - Proven ability to work on air-gapped systems with no external package repositories - Experience with GPU orchestration (NVIDIA A100/H100) and CUDA optimisation - Python expertise with offline dependency management and local package mirrors Technical Stack (All On-Premises) Models: Llama 3, Mistral, Qwen (locally hosted) Vector Stores: Chroma, FAISS, Milvus Orchestration: LangChain, LangGraph for agents Hosting: vLLM, TGI, Ollama on bare metal/private cloud Infrastructure: Air-gapped Kubernetes, local container registries Desirable Skills - Experience with defence/government IT security protocols - Knowledge of CIS benchmarks and NCSC guidelines - Familiarity with cross-domain solutions and data diodes - Understanding of classification marking and handling procedures