Palantir Foundry Consultant You'll work as a hands-on Palantir Foundry consultant, helping to design, build and support data and application workflows on the platform. You'll work closely with senior Foundry engineers and architects, contribute to technical decisions, and collaborate directly with business stakeholders as you grow into owning areas end-to-end. Core Responsibilities Foundry Solution Delivery Contribute to solution design and implementation across: Data integration (Pipeline Builder/code-based pipelines, connectors, incremental loads). Ontology modelling (objects, relationships, basic semantics, versioning). Application layer (Workshop, Code Repositories, OSDK/APIs, Actions, AIP/agentic workflows). Implement data modelling and transformation patterns under guidance from senior team members. Help configure permissions (RBAC/ABAC), object-level security and auditability following established standards. Support CI/CD and environment promotion processes for Foundry artefacts. Scalability, Reliability & Operations Help investigate performance issues (eg parallelisation, partitioning, caching, compute configuration) with mentorship from more senior colleagues. Contribute to monitoring, alerting and observability setup for pipelines, applications and integrations. Participate in incident response and root cause analysis for platform and application issues. Assist in applying non-functional requirements (SLA/SLOs, resilience, backup and recovery) defined by senior engineers. Client-Facing Engineering & Stakeholder Support Join technical discovery sessions to help translate business needs into Foundry use cases. Prepare and demo prototypes, data flows and applications to technical and business users. Support integration work with existing enterprise systems (data warehouses, APIs, identity providers) under guidance. Enablement, Collaboration & Standards Take part in and later help deliver technical enablement sessions, hands-on labs and onboarding for analysts and power users. Share learning, debugging tips and best practices with peers. Follow internal standards for code quality, naming conventions, testing and design patterns and contribute improvements as you gain experience. Required Experience Commercial experience working with Palantir Foundry in an implementation, consulting or engineering role. Hands-on experience with: Building and maintaining Foundry pipelines and/or ontologies. Developing or supporting Foundry applications (Workshop, Code Repos, OSDK, Actions). Exposure to at least one production Foundry project, contributing to: Solution implementation. Deployment or promotion through environments. Operational support and troubleshooting. Experience dealing with: Performance issues or optimisation tasks. Permissions and basic security models. Schema/ontology changes and their impact on downstream use cases. Integration problems and incident recovery in collaboration with senior staff. Some client-facing experience (eg workshops, demos, requirement sessions or design walkthroughs). Any experience helping to train, onboard or support other Foundry users (eg internal sessions, documentation, brown-bag talks) is a plus. Familiarity with Foundry's constraints and common pitfalls, and willingness to learn deeper platform internals, limitations and workarounds. Technical Skills Practical experience with core Foundry components such as: Pipeline Builder Ontology Workshop Code Repositories OSDK/APIs Actions and AIP/agentic features Proficiency in at least one general-purpose programming language commonly used with Foundry (eg Python, Java or similar) for data transformations, services and integrations. Solid understanding of core data engineering concepts: batch/stream processing basics, data modelling, data quality and governance. Experience (or strong interest) in integrating Foundry with: Enterprise identity (SSO, SAML/OIDC) REST APIs and services Existing data platforms or warehouses Familiarity with modern software engineering practices: Version control and code review Automated testing CI/CD pipelines Infrastructure-as-code concepts (where applicable)
Jan 29, 2026
Full time
Palantir Foundry Consultant You'll work as a hands-on Palantir Foundry consultant, helping to design, build and support data and application workflows on the platform. You'll work closely with senior Foundry engineers and architects, contribute to technical decisions, and collaborate directly with business stakeholders as you grow into owning areas end-to-end. Core Responsibilities Foundry Solution Delivery Contribute to solution design and implementation across: Data integration (Pipeline Builder/code-based pipelines, connectors, incremental loads). Ontology modelling (objects, relationships, basic semantics, versioning). Application layer (Workshop, Code Repositories, OSDK/APIs, Actions, AIP/agentic workflows). Implement data modelling and transformation patterns under guidance from senior team members. Help configure permissions (RBAC/ABAC), object-level security and auditability following established standards. Support CI/CD and environment promotion processes for Foundry artefacts. Scalability, Reliability & Operations Help investigate performance issues (eg parallelisation, partitioning, caching, compute configuration) with mentorship from more senior colleagues. Contribute to monitoring, alerting and observability setup for pipelines, applications and integrations. Participate in incident response and root cause analysis for platform and application issues. Assist in applying non-functional requirements (SLA/SLOs, resilience, backup and recovery) defined by senior engineers. Client-Facing Engineering & Stakeholder Support Join technical discovery sessions to help translate business needs into Foundry use cases. Prepare and demo prototypes, data flows and applications to technical and business users. Support integration work with existing enterprise systems (data warehouses, APIs, identity providers) under guidance. Enablement, Collaboration & Standards Take part in and later help deliver technical enablement sessions, hands-on labs and onboarding for analysts and power users. Share learning, debugging tips and best practices with peers. Follow internal standards for code quality, naming conventions, testing and design patterns and contribute improvements as you gain experience. Required Experience Commercial experience working with Palantir Foundry in an implementation, consulting or engineering role. Hands-on experience with: Building and maintaining Foundry pipelines and/or ontologies. Developing or supporting Foundry applications (Workshop, Code Repos, OSDK, Actions). Exposure to at least one production Foundry project, contributing to: Solution implementation. Deployment or promotion through environments. Operational support and troubleshooting. Experience dealing with: Performance issues or optimisation tasks. Permissions and basic security models. Schema/ontology changes and their impact on downstream use cases. Integration problems and incident recovery in collaboration with senior staff. Some client-facing experience (eg workshops, demos, requirement sessions or design walkthroughs). Any experience helping to train, onboard or support other Foundry users (eg internal sessions, documentation, brown-bag talks) is a plus. Familiarity with Foundry's constraints and common pitfalls, and willingness to learn deeper platform internals, limitations and workarounds. Technical Skills Practical experience with core Foundry components such as: Pipeline Builder Ontology Workshop Code Repositories OSDK/APIs Actions and AIP/agentic features Proficiency in at least one general-purpose programming language commonly used with Foundry (eg Python, Java or similar) for data transformations, services and integrations. Solid understanding of core data engineering concepts: batch/stream processing basics, data modelling, data quality and governance. Experience (or strong interest) in integrating Foundry with: Enterprise identity (SSO, SAML/OIDC) REST APIs and services Existing data platforms or warehouses Familiarity with modern software engineering practices: Version control and code review Automated testing CI/CD pipelines Infrastructure-as-code concepts (where applicable)
Data & AI Senior Consultants Location - We are flexible: onsite, hybrid or fully remote, depending on what works for you and the client, UK or Netherlands based. What you will actually be doing This is not a role where you build clever models that never get used. Your focus is on creating measurable value for clients using data science, machine learning and GenAI, in a consulting and advisory context. You will own work from the very beginning, asking questions like "What value are we trying to create here?" and "Is this the right problem to solve?" through to "It is live, stakeholders are using it and we can see the impact in the numbers." You will work fairly independently and you will also be someone that more junior team members look to for help and direction. A big part of the job is taking messy, ambiguous business and technical problems and turning them into clear, valuable solutions that make sense to the client. You will do this in a client facing role. That means you will be in the room for key conversations, providing honest advice, managing expectations and helping clients make good decisions about where and how to use AI. What your day to day might look like Getting to the heart of the problem Meeting with stakeholders who may not be clear on what they really need Using discovery sessions, workshops and structured questioning to uncover the real business problem Framing success in terms of value. For example higher revenue, lower cost, reduced risk, increased efficiency or better customer experience Translating business goals into a clear roadmap of data and AI work that everyone can understand Advising clients when AI is not the right solution and suggesting simpler or more cost effective alternatives Consulting and advisory work Acting as a trusted advisor to product owners, heads of department and executives Helping clients prioritise use cases based on value, feasibility and risk Communicating trade offs in a simple way. For example accuracy versus speed, innovation versus compliance, cost versus impact Preparing and delivering client presentations, proposals and updates that tell a clear story Supporting pre sales activities where needed, such as scoping work, estimating effort and defining outcomes Managing client expectations, risks and dependencies so there are no surprises Building things that actually work Once the problem and value are clear, you will design and deliver production ready ML and GenAI solutions. That includes: Designing and building data pipelines, batch or streaming, that support the desired outcomes Working with engineers and architects so your work fits cleanly into existing systems Making sure what you build is reliable in production and moves the needle on agreed metrics, not just offline benchmarks Explaining design decisions to both technical and non technical stakeholders GenAI work You will work with GenAI in ways that are grounded in real use cases and business value: Building RAG systems that improve search, content discovery or productivity rather than existing for their own sake Implementing guardrails so models do not leak PII or generate harmful or off brand content Defining and tracking the right metrics so you and the client can see whether a GenAI solution is useful and cost effective Fine tuning and optimising models so they perform well for the use case and budget Designing agentic workflows where they genuinely improve outcomes rather than add complexity Helping clients understand what GenAI can and cannot do in practice Keeping it running You will set up the foundations that protect value over time: Experiment tracking and model versioning so you know what works and can roll back safely CI/CD pipelines for ML so improvements reach users quickly and reliably Monitoring and alerting for models and data so you can catch issues before they damage trust or results Communicating operational risks and mitigations to non technical stakeholders in plain language Security, quality and compliance You will help make sure: Data is accurate, traceable and well managed so decisions are sound Sensitive data is handled correctly, protecting users and the business Regulatory and compliance requirements are met, avoiding costly mistakes Clients understand the risk profile of AI solutions and the controls in place Working with people You will be a bridge between technical and non technical teams, inside our organisation and on the client side. That means: Explaining complex ML and GenAI ideas in plain language, always tied to business outcomes Working closely with product managers, engineers and business stakeholders to prioritise work that matters Facilitating workshops, playback sessions and show and tells that build buy in and understanding Coaching and supporting junior colleagues so the whole team can deliver more value Representing the company professionally in client meetings and at industry events What we are looking for Experience Around 3 to 6 years of experience shipping ML or GenAI solutions into production A track record of seeing projects through from discovery to delivery, with clear impact Experience working directly with stakeholders or clients in a consulting, advisory or product facing role Education A Bachelor or Master degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics or Engineering or Equivalent experience that shows you can deliver results Technical skills Core skills Strong Python and SQL, with clean, maintainable code Solid understanding of ML fundamentals. For example feature engineering, model selection, handling imbalanced data, choosing and interpreting metrics Experience with PyTorch or TensorFlow GenAI specific Hands on experience with LLM APIs or open source models such as Llama or Mistral Experience building RAG systems with vector databases such as FAISS, Pinecone or Weaviate Ability to evaluate and improve prompts and retrieval quality using clear metrics Understanding of safety practices such as PII redaction and content filtering Exposure to agentic frameworks Cloud and infrastructure Comfortable working in at least one major cloud provider. AWS, GCP or Azure Familiar with Docker and CI/CD pipelines Experience with managed ML platforms such as SageMaker, Vertex AI or Azure ML Data engineering and MLOps Experience with data warehouses such as Snowflake, BigQuery or Redshift Workflow orchestration using tools like Airflow or Dagster Experience with MLOps tools such as MLflow, Weights and Biases or similar Awareness of data and model drift, and how to monitor and respond to it before it erodes value Soft skills, the things that really matter You are comfortable in client facing settings and can build trust quickly You can talk with anyone from a CEO to a new data analyst, and always bring the conversation back to business value You can take a vague, messy business problem and turn it into a clear technical plan that links to outcomes and metrics You are happy to push back and challenge assumptions respectfully when it is in the client's best interest You like helping other people grow and are happy to mentor junior colleagues You communicate clearly in writing and in person Nice to have, not required Do not rule yourself out if you do not have these. They are a bonus, not a checklist. Experience with Delta Lake, Iceberg, Spark or Databricks, Palantir Experience optimising LLM serving with tools such as vLLM, TGI or TensorRT LLM Search and ranking experience. For example Elasticsearch or rerankers Background in time series forecasting, causal inference, recommender systems or optimisation Experience managing cloud costs and IAM so value is not lost to waste Ability to work in other languages where needed. For example Java, Scala, Go or bash Experience with BI tools such as Looker or Tableau Prior consulting experience or leading client projects end to end Contributions to open source, conference talks or published papers that show your ability to share ideas and influence the wider community Got a background that fits and you're up for a new challenge? Send over your latest CV, expectations and availability. Staffworx Limited is a UK based recruitment consultancy partnering with leading global brands across digital, AI, software, and business consulting. Let's talk about what you could add to the mix.
Jan 29, 2026
Full time
Data & AI Senior Consultants Location - We are flexible: onsite, hybrid or fully remote, depending on what works for you and the client, UK or Netherlands based. What you will actually be doing This is not a role where you build clever models that never get used. Your focus is on creating measurable value for clients using data science, machine learning and GenAI, in a consulting and advisory context. You will own work from the very beginning, asking questions like "What value are we trying to create here?" and "Is this the right problem to solve?" through to "It is live, stakeholders are using it and we can see the impact in the numbers." You will work fairly independently and you will also be someone that more junior team members look to for help and direction. A big part of the job is taking messy, ambiguous business and technical problems and turning them into clear, valuable solutions that make sense to the client. You will do this in a client facing role. That means you will be in the room for key conversations, providing honest advice, managing expectations and helping clients make good decisions about where and how to use AI. What your day to day might look like Getting to the heart of the problem Meeting with stakeholders who may not be clear on what they really need Using discovery sessions, workshops and structured questioning to uncover the real business problem Framing success in terms of value. For example higher revenue, lower cost, reduced risk, increased efficiency or better customer experience Translating business goals into a clear roadmap of data and AI work that everyone can understand Advising clients when AI is not the right solution and suggesting simpler or more cost effective alternatives Consulting and advisory work Acting as a trusted advisor to product owners, heads of department and executives Helping clients prioritise use cases based on value, feasibility and risk Communicating trade offs in a simple way. For example accuracy versus speed, innovation versus compliance, cost versus impact Preparing and delivering client presentations, proposals and updates that tell a clear story Supporting pre sales activities where needed, such as scoping work, estimating effort and defining outcomes Managing client expectations, risks and dependencies so there are no surprises Building things that actually work Once the problem and value are clear, you will design and deliver production ready ML and GenAI solutions. That includes: Designing and building data pipelines, batch or streaming, that support the desired outcomes Working with engineers and architects so your work fits cleanly into existing systems Making sure what you build is reliable in production and moves the needle on agreed metrics, not just offline benchmarks Explaining design decisions to both technical and non technical stakeholders GenAI work You will work with GenAI in ways that are grounded in real use cases and business value: Building RAG systems that improve search, content discovery or productivity rather than existing for their own sake Implementing guardrails so models do not leak PII or generate harmful or off brand content Defining and tracking the right metrics so you and the client can see whether a GenAI solution is useful and cost effective Fine tuning and optimising models so they perform well for the use case and budget Designing agentic workflows where they genuinely improve outcomes rather than add complexity Helping clients understand what GenAI can and cannot do in practice Keeping it running You will set up the foundations that protect value over time: Experiment tracking and model versioning so you know what works and can roll back safely CI/CD pipelines for ML so improvements reach users quickly and reliably Monitoring and alerting for models and data so you can catch issues before they damage trust or results Communicating operational risks and mitigations to non technical stakeholders in plain language Security, quality and compliance You will help make sure: Data is accurate, traceable and well managed so decisions are sound Sensitive data is handled correctly, protecting users and the business Regulatory and compliance requirements are met, avoiding costly mistakes Clients understand the risk profile of AI solutions and the controls in place Working with people You will be a bridge between technical and non technical teams, inside our organisation and on the client side. That means: Explaining complex ML and GenAI ideas in plain language, always tied to business outcomes Working closely with product managers, engineers and business stakeholders to prioritise work that matters Facilitating workshops, playback sessions and show and tells that build buy in and understanding Coaching and supporting junior colleagues so the whole team can deliver more value Representing the company professionally in client meetings and at industry events What we are looking for Experience Around 3 to 6 years of experience shipping ML or GenAI solutions into production A track record of seeing projects through from discovery to delivery, with clear impact Experience working directly with stakeholders or clients in a consulting, advisory or product facing role Education A Bachelor or Master degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics or Engineering or Equivalent experience that shows you can deliver results Technical skills Core skills Strong Python and SQL, with clean, maintainable code Solid understanding of ML fundamentals. For example feature engineering, model selection, handling imbalanced data, choosing and interpreting metrics Experience with PyTorch or TensorFlow GenAI specific Hands on experience with LLM APIs or open source models such as Llama or Mistral Experience building RAG systems with vector databases such as FAISS, Pinecone or Weaviate Ability to evaluate and improve prompts and retrieval quality using clear metrics Understanding of safety practices such as PII redaction and content filtering Exposure to agentic frameworks Cloud and infrastructure Comfortable working in at least one major cloud provider. AWS, GCP or Azure Familiar with Docker and CI/CD pipelines Experience with managed ML platforms such as SageMaker, Vertex AI or Azure ML Data engineering and MLOps Experience with data warehouses such as Snowflake, BigQuery or Redshift Workflow orchestration using tools like Airflow or Dagster Experience with MLOps tools such as MLflow, Weights and Biases or similar Awareness of data and model drift, and how to monitor and respond to it before it erodes value Soft skills, the things that really matter You are comfortable in client facing settings and can build trust quickly You can talk with anyone from a CEO to a new data analyst, and always bring the conversation back to business value You can take a vague, messy business problem and turn it into a clear technical plan that links to outcomes and metrics You are happy to push back and challenge assumptions respectfully when it is in the client's best interest You like helping other people grow and are happy to mentor junior colleagues You communicate clearly in writing and in person Nice to have, not required Do not rule yourself out if you do not have these. They are a bonus, not a checklist. Experience with Delta Lake, Iceberg, Spark or Databricks, Palantir Experience optimising LLM serving with tools such as vLLM, TGI or TensorRT LLM Search and ranking experience. For example Elasticsearch or rerankers Background in time series forecasting, causal inference, recommender systems or optimisation Experience managing cloud costs and IAM so value is not lost to waste Ability to work in other languages where needed. For example Java, Scala, Go or bash Experience with BI tools such as Looker or Tableau Prior consulting experience or leading client projects end to end Contributions to open source, conference talks or published papers that show your ability to share ideas and influence the wider community Got a background that fits and you're up for a new challenge? Send over your latest CV, expectations and availability. Staffworx Limited is a UK based recruitment consultancy partnering with leading global brands across digital, AI, software, and business consulting. Let's talk about what you could add to the mix.
AI Architect, Lead Agentic Engineer Occasional travel client offices and two trips to London HQ per month Lead the design and delivery of AI-native transformation initiatives for insurance clients, spanning agentic systems, retrieval architectures, semantic layers and decision intelligence. This is a senior, hands-on consulting role combining deep AI engineering expertise with strong client-facing presence, shaping both insurance-specific client outcomes and the firm's long-term AI engineering capability. As demand accelerates across claims automation, underwriting decision support, policy servicing, fraud detection, compliance and operational efficiency, the consulting practice is expanding its engineering capability across agentic systems, retrieval, ontologies and AI-enabled execution within regulated insurance environments. The Consulting Engineer is a hands-on AI systems builder who combines engineering depth with commercial and product thinking to design, build and deploy LLM- and agent-driven solutions for insurers, brokers and value chain partners. Key Accountabilities Client-Facing AI Engineering & Agentic System Design You will design and deliver production-grade AI systems for insurance clients, including: LLM-powered applications for claims handling, underwriting support, policy servicing, document processing and customer operations Multi-agent architectures for insurance workflows, including triage, decision support, escalation, delegation and human-in-the-loop controls Retrieval and vector-based systems over policy wordings, endorsements, claims files, loss runs, underwriting guidelines and regulatory documentation Semantic layers, ontologies and knowledge models aligned to insurance data structures, coverage logic and risk taxonomies Integrations with core insurance platforms (claims systems, PAS, underwriting workbenches), data warehouses and third-party providers Prompt engineering at scale with regulatory guardrails, explainability, traceability and auditability Safety constraints for hallucination control, coverage interpretation accuracy and customer-facing use cases You will lead technical design within client engagements and set architectural direction across delivery pods. Technical Discovery, Feasibility & Solution Architecture Working closely with consulting counterparts, you will: Translate ambiguous insurance challenges into clear, feasible AI architectures Assess client data maturity, policy document quality, Legacy platforms and security constraints Shape use cases across claims leakage reduction, underwriting efficiency, fraud detection and compliance automation Work directly with insurance SMEs to surface edge cases, exceptions, regulatory nuances and operational realities Produce clear, concise technical artefacts suitable for regulated, risk-aware client audiences Delivery Excellence, AI Ops & Reliability (Regulated Environments) You will ensure solutions are enterprise-ready and regulator-safe by: Implementing evaluation frameworks for accuracy, coverage interpretation, decision consistency and bias Designing monitoring, logging and tracing suitable for regulated insurance environments Applying governance, risk and compliance principles (eg audit trails, explainability, access controls) Supporting controlled releases and operational handover into insurer IT and operations teams Ensuring reliability, reproducibility, performance and cost discipline at insurance scale Reusable Assets & Insurance AI Capability Building As part of a consulting-led engineering practice, you will: Build reusable insurance-specific accelerators, agent patterns and reference architectures Contribute to internal playbooks covering claims, underwriting, policy servicing and compliance use cases Share emerging research, frameworks and AI trends relevant to the insurance sector Influence delivery methodology, technical standards and agentic design patterns for regulated industries Experience & Skills This is a senior, hands-on consulting engineering role. Candidates should bring: Experience in software engineering, AI engineering or applied data engineering Strong hands-on experience with LLMs, embeddings, RAG pipelines and vector databases Experience designing or implementing multi-agent systems or tool-calling frameworks Strong Python skills with experience building production-grade, regulated systems Experience with at least one major cloud AI ecosystem (Azure/OpenAI, GCP/Vertex, AWS, Anthropic) Familiarity with semantic modelling, ontologies or knowledge graph concepts, ideally applied to complex domains Proven ability to rapidly prototype and validate solutions with business stakeholders Experience working directly with clients in consulting, professional services or regulated enterprise environments Insurance domain experience (claims, underwriting, policy, risk, compliance or adjacent systems) strongly preferred Staffworx are a UK based Talent & Recruiting Partner, supporting Digital Commerce, Software and Value Add Consulting sectors across the UK & EMEA.
Jan 28, 2026
Full time
AI Architect, Lead Agentic Engineer Occasional travel client offices and two trips to London HQ per month Lead the design and delivery of AI-native transformation initiatives for insurance clients, spanning agentic systems, retrieval architectures, semantic layers and decision intelligence. This is a senior, hands-on consulting role combining deep AI engineering expertise with strong client-facing presence, shaping both insurance-specific client outcomes and the firm's long-term AI engineering capability. As demand accelerates across claims automation, underwriting decision support, policy servicing, fraud detection, compliance and operational efficiency, the consulting practice is expanding its engineering capability across agentic systems, retrieval, ontologies and AI-enabled execution within regulated insurance environments. The Consulting Engineer is a hands-on AI systems builder who combines engineering depth with commercial and product thinking to design, build and deploy LLM- and agent-driven solutions for insurers, brokers and value chain partners. Key Accountabilities Client-Facing AI Engineering & Agentic System Design You will design and deliver production-grade AI systems for insurance clients, including: LLM-powered applications for claims handling, underwriting support, policy servicing, document processing and customer operations Multi-agent architectures for insurance workflows, including triage, decision support, escalation, delegation and human-in-the-loop controls Retrieval and vector-based systems over policy wordings, endorsements, claims files, loss runs, underwriting guidelines and regulatory documentation Semantic layers, ontologies and knowledge models aligned to insurance data structures, coverage logic and risk taxonomies Integrations with core insurance platforms (claims systems, PAS, underwriting workbenches), data warehouses and third-party providers Prompt engineering at scale with regulatory guardrails, explainability, traceability and auditability Safety constraints for hallucination control, coverage interpretation accuracy and customer-facing use cases You will lead technical design within client engagements and set architectural direction across delivery pods. Technical Discovery, Feasibility & Solution Architecture Working closely with consulting counterparts, you will: Translate ambiguous insurance challenges into clear, feasible AI architectures Assess client data maturity, policy document quality, Legacy platforms and security constraints Shape use cases across claims leakage reduction, underwriting efficiency, fraud detection and compliance automation Work directly with insurance SMEs to surface edge cases, exceptions, regulatory nuances and operational realities Produce clear, concise technical artefacts suitable for regulated, risk-aware client audiences Delivery Excellence, AI Ops & Reliability (Regulated Environments) You will ensure solutions are enterprise-ready and regulator-safe by: Implementing evaluation frameworks for accuracy, coverage interpretation, decision consistency and bias Designing monitoring, logging and tracing suitable for regulated insurance environments Applying governance, risk and compliance principles (eg audit trails, explainability, access controls) Supporting controlled releases and operational handover into insurer IT and operations teams Ensuring reliability, reproducibility, performance and cost discipline at insurance scale Reusable Assets & Insurance AI Capability Building As part of a consulting-led engineering practice, you will: Build reusable insurance-specific accelerators, agent patterns and reference architectures Contribute to internal playbooks covering claims, underwriting, policy servicing and compliance use cases Share emerging research, frameworks and AI trends relevant to the insurance sector Influence delivery methodology, technical standards and agentic design patterns for regulated industries Experience & Skills This is a senior, hands-on consulting engineering role. Candidates should bring: Experience in software engineering, AI engineering or applied data engineering Strong hands-on experience with LLMs, embeddings, RAG pipelines and vector databases Experience designing or implementing multi-agent systems or tool-calling frameworks Strong Python skills with experience building production-grade, regulated systems Experience with at least one major cloud AI ecosystem (Azure/OpenAI, GCP/Vertex, AWS, Anthropic) Familiarity with semantic modelling, ontologies or knowledge graph concepts, ideally applied to complex domains Proven ability to rapidly prototype and validate solutions with business stakeholders Experience working directly with clients in consulting, professional services or regulated enterprise environments Insurance domain experience (claims, underwriting, policy, risk, compliance or adjacent systems) strongly preferred Staffworx are a UK based Talent & Recruiting Partner, supporting Digital Commerce, Software and Value Add Consulting sectors across the UK & EMEA.
Palantir Foundry Consultant - You will design and build production-grade data and application workflows on Foundry, own technical decisions end-to-end and work directly with senior stakeholders while mentoring engineering teams. Core Responsibilities Foundry Solution Design & Build End-to-end solution design across: Data integration (Pipeline Builder/code-based pipelines, connectors, incremental loads). Ontology modelling (object/relationship design, semantics, versioning). Application layer (Workshop, Code Repositories, OSDK/APIs, Actions, AIP/agentic workflows). Define and implement patterns for data modelling, transformation, and lineage tracking. Design permission models (RBAC/ABAC), object-level security and auditability. Implement CI/CD and environment promotion strategies for Foundry artefacts. Run technical discovery with senior stakeholders to translate business needs into concrete Foundry use cases. Provide technical guidance on integration with existing enterprise systems (data warehouses, message buses, APIs, identity providers). Technical Skills Strong practical knowledge of core Foundry components: Pipeline Builder, Ontology, Workshop, Code Repositories, OSDK, Actions, AIP/agentic features. Proficiency in at least one general-purpose programming language commonly used with Foundry (eg Python, Java, or similar) for transformations, services, and integrations. Solid background in data engineering concepts: batch/stream processing, data modelling, data quality, and governance. Experience integrating Foundry with enterprise identity (SSO, SAML/OIDC), APIs, and existing data platforms. Familiarity with modern software engineering practices: version control, code review, automated testing, CI/CD, infrastructure-as-code (where applicable). Staffworx are a UK based Talent & Recruiting Partner, supporting Digital Commerce, Software and Value Add Consulting sectors across the UK & EMEA.
Jan 27, 2026
Full time
Palantir Foundry Consultant - You will design and build production-grade data and application workflows on Foundry, own technical decisions end-to-end and work directly with senior stakeholders while mentoring engineering teams. Core Responsibilities Foundry Solution Design & Build End-to-end solution design across: Data integration (Pipeline Builder/code-based pipelines, connectors, incremental loads). Ontology modelling (object/relationship design, semantics, versioning). Application layer (Workshop, Code Repositories, OSDK/APIs, Actions, AIP/agentic workflows). Define and implement patterns for data modelling, transformation, and lineage tracking. Design permission models (RBAC/ABAC), object-level security and auditability. Implement CI/CD and environment promotion strategies for Foundry artefacts. Run technical discovery with senior stakeholders to translate business needs into concrete Foundry use cases. Provide technical guidance on integration with existing enterprise systems (data warehouses, message buses, APIs, identity providers). Technical Skills Strong practical knowledge of core Foundry components: Pipeline Builder, Ontology, Workshop, Code Repositories, OSDK, Actions, AIP/agentic features. Proficiency in at least one general-purpose programming language commonly used with Foundry (eg Python, Java, or similar) for transformations, services, and integrations. Solid background in data engineering concepts: batch/stream processing, data modelling, data quality, and governance. Experience integrating Foundry with enterprise identity (SSO, SAML/OIDC), APIs, and existing data platforms. Familiarity with modern software engineering practices: version control, code review, automated testing, CI/CD, infrastructure-as-code (where applicable). Staffworx are a UK based Talent & Recruiting Partner, supporting Digital Commerce, Software and Value Add Consulting sectors across the UK & EMEA.
Amazon DynamoDB NoSQL Database Consultant to join a leading enterprise client's team, working on their next-generation software platform. As part of the SRE/DBA team, you'll take ownership of designing, implementing and optimising DynamoDB-based solutions for high-performance, scalable applications. Key Responsibilities Design & implement scalable data models using DynamoDB Optimise table structures, partition/sort keys for performance & cost-efficiency Define and maintain best practices for DynamoDB usage Support migrations from other databases to DynamoDB Build backup & disaster recovery solutions Monitor & optimise performance, capacity, and costs Provide technical leadership and mentoring on DynamoDB topics Experience Required Strong background with NoSQL Specialist hands-on Amazon DynamoDB experience Expertise in partition keys, sort keys, indexes, capacity modes Skilled in programming languages (Java, Python, Node.js) Experience with AWS SDK/CLI & related AWS services (Lambda, API Gateway, CloudWatch) Strong knowledge of distributed systems Nice to Have AWS Certified Database Specialty DynamoDB Streams + Lambda integration DynamoDB Global Tables (multi-region) DynamoDB Accelerator (DAX) Security best practices (encryption at rest & in transit) If you're interested, please send your CV along with your rate & availability . Staffworx Limited are a UK-based recruitment consultancy supporting the global E-commerce, software & consulting sectors.
Sep 23, 2025
Contractor
Amazon DynamoDB NoSQL Database Consultant to join a leading enterprise client's team, working on their next-generation software platform. As part of the SRE/DBA team, you'll take ownership of designing, implementing and optimising DynamoDB-based solutions for high-performance, scalable applications. Key Responsibilities Design & implement scalable data models using DynamoDB Optimise table structures, partition/sort keys for performance & cost-efficiency Define and maintain best practices for DynamoDB usage Support migrations from other databases to DynamoDB Build backup & disaster recovery solutions Monitor & optimise performance, capacity, and costs Provide technical leadership and mentoring on DynamoDB topics Experience Required Strong background with NoSQL Specialist hands-on Amazon DynamoDB experience Expertise in partition keys, sort keys, indexes, capacity modes Skilled in programming languages (Java, Python, Node.js) Experience with AWS SDK/CLI & related AWS services (Lambda, API Gateway, CloudWatch) Strong knowledge of distributed systems Nice to Have AWS Certified Database Specialty DynamoDB Streams + Lambda integration DynamoDB Global Tables (multi-region) DynamoDB Accelerator (DAX) Security best practices (encryption at rest & in transit) If you're interested, please send your CV along with your rate & availability . Staffworx Limited are a UK-based recruitment consultancy supporting the global E-commerce, software & consulting sectors.
