276 to 300 of 337 Retrieval-Augmented Generation Jobs in the UK excluding London

Build and maintain inference-time integrations such as model gateways, APIs, caching, fallbacks, timeouts, and concurrency controls for production AI systems Implement retrieval-augmented generation components where applicable, including chunking, embeddings, retrieval, and grounding strategies Build REST and gRPC endpoints following agreed … cause analysis Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards Apply knowledge of tools within ...

/ELT, SQL and Python. Analytics and BI - Power BI, semantic modelling, governance and performance. Applied and Generative AI on Azure - retrieval-augmented generation, Azure OpenAI, Azure AI Search. Consulting skills to match - shaping requirements, engaging stakeholders, and delivering to a high standard. Engagements ...

including API design, automated testing, asynchronous processing, dependency management and production debugging. Practical experience with agent orchestration, structured tool calling, workflow state, retrieval-augmented generation and human-in-the-loop patterns. Experience building secure integrations with enterprise APIs, repositories, cloud services, CI/ ...

with Azure OpenAI, Azure AI Document Intelligence or comparable cloud-based AI and document-processing services. Experience designing solutions involving generative AI, retrieval-augmented generation or AI agents. Knowledge of secure prompt design, model governance, data privacy and responsible AI controls. Experience with ...

build and deploy enterprise-grade AI solutions across an Enterprise Knowledge Base (EKB). This is a hands-on role focused on GenAI, LLMs, RAG and Agentic AI, taking solutions from prototype through to production. You'll work across AI, data and engineering teams to build intelligent retrieval, knowledge discovery and automation capabilities. Key Responsibilities Build production-grade GenAI, LLM, RAG and Agentic AI solutions Develop AI Agents and Multi-Agent Systems Work with LangChain, LangGraph and MCP Build intelligent retrieval using Embeddings, Vector Databases and Semantic Search Apply Prompt & Context Engineering Integrate ...

Tech Lead level, with the ability to be both highly technical and an effective people manager Hands-on experience building AI/ML applications (RAG, agent frameworks, MCPs, skills, harnesses) and taking them to production or in platforming (LLM or MCP gateway, agentic runtime, auth, data retrieval, eval … About the Team Our AI Strategy and Innovation team shapes Moody's product ecosystem through AI, data, and strategic partnerships to deliver next-generation solutions. The team embeds AI across product strategy using large language models, agentic platforms, and advanced analytics to enhance decision-making and client outcomes. ...

Designing APIs, services and integrations to connect AI capabilities with wider enterprise systems. Building agentic AI applications, including multi-agent workflows and orchestration. Developing RAG solutions using vector search, enterprise data and retrieval techniques. Writing clean, maintainable and well-tested production code. Working with CI/CD pipelines … building AI-enabled applications using technologies such as Azure AI, Microsoft AI Foundry, Azure OpenAI, Semantic Kernel, Agent Framework or similar. Understanding of RAG, LLMs, vector search and agentic AI concepts. Experience integrating applications with APIs, cloud services and enterprise systems. Strong understanding of DevOps, CI/CD, source control ...

Solution Delivery Lead the design, development and deployment of AI-integrated applications using modern full-stack technologies. Build features powered by LLMs, agentic workflows, RAG patterns and MCP integrations. Work directly with customers to shape requirements and translate them into working software. Integrate Azure AI services such as Azure OpenAI …/Node.js, .NET/C#, Python/FastAPI). Proven background delivering production software in client-facing environments. Experience with LLM integration, prompt engineering, RAG or agentic AI development. Familiarity with AI-assisted development tools (GitHub Copilot, Claude, Cursor). Hands-on experience with Azure cloud services and CI/ ...

mainly Python across the modern AI engineering stack - harness engineering, skills and tool building, agent workflows and orchestration, agent hosting and sandboxing, guardrails, evals, RAG and context engineering, and tokenomics (cost, latency, model selection). Production-grade LLM system experience is the core requirement. You will work on three types … your primary language. LLM-powered systems in production: real-world experience working with models via APIs - harnesses, orchestration, structured output, tool use and agents, RAG where appropriate. Evals and quality: designing evaluation sets, measuring accuracy, recall and precision for LLM steps, regression-testing prompts and workflows. Safety and guardrails ...

Strands Agents Build, integrate, and maintain MCP (Model Context Protocol) servers that expose capabilities to AI agents across client platforms Architect and implement RAG pipelines using Amazon Bedrock Knowledge Bases, managing vector stores, embeddings, and document ingestion from S3 and other sources Apply the A2A (Agent-to-Agent) protocol … integrated MCP servers in a production or near-production context Strands Agents — familiarity with the framework and its application to agentic workflows on AWS RAG implementation — knowledge base design, chunking strategy, vector store configuration, and retrieval evaluation Python — strong, applied proficiency in an AWS and AI context ...

concept through to production deployment Designing and developing AI and Machine Learning solutions using Azure and modern AI technologies Building production-ready LLM applications, RAG pipelines and AI agents Working closely with Data Engineering teams to prepare and integrate structured and unstructured data Collaborating with Infrastructure teams to deploy secure … Science and Machine Learning Experience designing and deploying production AI solutions Strong knowledge of AI, Machine Learning and Large Language Models (LLMs) Experience developing RAG solutions and AI agents Excellent Python skills alongside modern AI/ML frameworks and SDKs Experience with Azure AI services or another enterprise AI cloud ...

for. What you'll be working on as an AI Engineer - LLM & Agentic Systems: Designing and building AI agents and agentic workflows - combining LLMs, RAG, reasoning, memory and tool orchestration to solve real, complex business problems. Owning delivery end-to-end - from initial exploration through to production deployment, integration … with clear, real-world examples you can speak to confidently. Hands-on experience building AI agents including tool use, orchestration and multi-step reasoning - RAG systems that actually deliver, not just proofs of concept. Strong Python skills and solid machine learning foundations - able to write clean, production-grade code with ...

plane for staff and customer authentication. Contribute to AI-enabled quotation and knowledge tools, including human-in-the-loop quote drafting and retrieval-augmented knowledge features. Collaborate closely with mobile, product and engineering colleagues where your APIs are consumed across different surfaces. Take part in architecture … through to delivery. Useful Additional Experience Exposure to React or React Native, particularly where APIs support web or mobile products. Experience with retrieval-augmented generation, vector search or integrating LLM-based features into production products. Experience working in a scale-up, transformation programme ...

looking for an experienced Agentic AI Engineer to join a pioneering team building next-generation AI solutions. You'll work on cutting-edge projects involving LLMs, Neo4j, Knowledge Graphs, GraphRAG, and AI Agents , helping unlock greater context, reasoning, and automation from their data. Key Responsibilities: Design and develop … teams to deliver production-ready AI systems Required Skills: Strong Python development experience Hands-on experience with Neo4j and Knowledge Graphs Experience with LLMs, RAG, or GraphRAG architectures Understanding of AI agents and autonomous workflows Cloud experience (AWS, Azure, or GCP) Desirable: LangGraph, LangChain, CrewAI, AutoGen, or similar frameworks Experience ...

Engineer to design, build, and deploy production-ready Generative AI systems. Role You will own the full lifecycle of GenAI applications, working with LLMs, RAG architectures, and agentic frameworks to deliver scalable, business-focused solutions. Responsibilities Build and deploy GenAI and RAG systems Develop agentic AI workflows Design prompts … clients and technical teams Requirements 1-3 years deploying LLM/GenAI systems in production Strong Python skills Experience with cloud platforms Knowledge of RAG, vector databases, and LLM frameworks Git and CI/CD experience Experience with LangChain and LangGraph AWS experience IT/Technology-related university degree (minimum ...

scalable application programming interfaces, microservices, and distributed systems Practical experience building enterprise AI applications using large language models, including agents, retrieval augmented generation, orchestration frameworks, and model optimization Demonstrated ability to own services, components, or features end to end, from technical design through production … from technical design through deployment, monitoring, production support, and continuous improvement Build and enhance large language model application features using retrieval augmented generation, orchestration frameworks, evaluation methods, agentic workflows, and tool integration Contribute to technical designs, participate in design reviews, and identify risks, constraints ...

Team" alongside civil servants, implementing the guardrails and standards set by the Lead AI Engineer and Lead Technical Architect. Key Responsibilities Build & Integrate Implement RAG pipelines, prompt templates, and agentic workflows, and wire them into application logic that connects with the department's Legacy and multi-cloud systems. Prompt Engineering … internal government engineers. Required Skills & Experience Practical experience building applications using LLMs or other foundation models, via API or self-hosted Experience with RAG architectures, vector databases, and embedding-based retrieval Solid general-purpose programming (commonly Python) and API design/integration skills Prompt engineering with a systematic ...

enable more reliable and scalable product development Establish end-to-end technical architectures for LLM-enabled systems, covering data pipelines, retrieval/RAG, prompt orchestration and agent workflows to support robust and effective AI implementation Analyse complex constraints across product, data, security, legal and operations, translating them into … services and the supporting platform services required for production delivery AI engineering experience delivering LLM-enabled systems using LLM APIs/Bedrock, including RAG architectures, vector databases and orchestration frameworks DevOps skills including CI/CD pipelines, containerisation and monitoring/observability, with experience defining release and operating practices ...

rare diseases affecting 400 million people globally, 90% of which have no approved treatment, Healx is on a mission to pioneer the next generation of drug discovery to help rare disease patients in need. We combine data, artificial intelligence and deep pharmacology expertise to develop treatments more quickly … write clear, tested, maintainable Python code that others can build on Fluency with the modern LLM/agent toolkit — model APIs, prompting, tool use, RAG, and the patterns this fast‐moving ecosystem is converging on (MCP, agent frameworks, evals). You enjoy working closely with non‐engineers ...

design and delivery of production-grade Generative AI and AI-powered solutions Owning architecture decisions across AI products and platforms Building and deploying LLM, RAG, and agentic AI systems at enterprise scale Evaluating build versus buy approaches and making pragmatic technology recommendations Working closely with engineering, security, governance, and business … commercial experience delivering Generative AI solutions into production Proven capability leading end-to-end AI projects from discovery through deployment Deep understanding of LLMs, RAG architectures, agentic AI, orchestration, prompting, and evaluation Strong software engineering foundations, including APIs, cloud architectures, and system design Experience making architecture and technology decisions within ...

teams to understand business and technical challenges. Design and develop AI-powered prototypes, proof-of-concepts and product solutions . Apply Generative AI, LLMs, RAG and prompt engineering to real-world use cases. Evaluate and integrate AI platforms, tools, APIs and cloud technologies. Contribute across the full product engineering lifecycle … experience across AI, software development and product engineering . Strong proficiency in Python and JavaScript . Hands-on experience with Generative AI, LLMs and RAG architectures . Experience with prompt engineering and AI APIs/integrations. Experience building prototypes, proof-of-concepts or production-ready solutions. Strong understanding of modern ...

teams to understand business and technical challenges. Design and develop AI-powered prototypes, proof-of-concepts and product solutions . Apply Generative AI, LLMs, RAG and prompt engineering to real-world use cases. Evaluate and integrate AI platforms, tools, APIs and cloud technologies. Contribute across the full product engineering lifecycle … experience across AI, software development and product engineering . Strong proficiency in Python and JavaScript . Hands-on experience with Generative AI, LLMs and RAG architectures . Experience with prompt engineering and AI APIs/integrations. Experience building prototypes, proof-of-concepts or production-ready solutions. Strong understanding of modern ...

human-in-the-loop design patterns. You maintain a current and informed perspective on the AI landscape---foundation models, agentic AI, retrieval-augmented generation, fine-tuning---and have a point of view on how these capabilities apply to regulated industries like clinical research. Preferred ...

challenge design decisions, and assess technical risk Deep expertise in enterprise AI applications built on large language models, including agents, retrieval augmented generation, orchestration frameworks, model optimization, and evaluation frameworks Hands-on experience building AI/ML applications (RAG, agent frameworks, MCPs, skills, harnesses … delivery outcomes across the portfolio Drive architectural decisions, organizational alignment, and integration of AI-powered solutions using large language models, retrieval augmented generation, orchestration frameworks, and agentic workflows Establish engineering excellence through architecture reviews, code reviews, automated testing, observability, security standards, operational excellence ...

agentic technologies. Key responsibilities Own the technical POC from scope through to demonstration Build front-end and back-end components Design and implement RAG and retrieval capabilities Work with vector databases and/or knowledge graphs Develop agentic workflows and supporting harnesses Build evaluation frameworks and assess … iterate and demonstrate solutions Work closely with architects, data specialists and client technical teams Experience required Strong hands-on AI/LLM engineering experience RAG and retrieval architectures Agentic AI/workflows Strong software engineering capability across front and back end Azure and Azure OpenAI/OpenAI Experience ...