101 to 125 of 279 Retrieval-Augmented Generation Jobs in England

/LLM systems to production. Strong Python + software engineering fundamentals (testing, code quality, CI/CD). Experience with modern LLM patterns (RAG, tool use/agents, fine-tuning or prompt systems) and robust evaluation. Practical experience operating production systems on-prem or in a private cloud (Linux, containers ...

success working with technical teams or senior specialists in high‐stakes modelling environments.* Deep understanding of AI/ML systems—including LLMs, agentic architectures, RAG pipelines, credit or pricing models, or risk modelling techniques.* Hands-on experience developing or validating models, performing statistical testing, and analysing model assumptions, limitations ...

modern vector databases. An understanding of Site Reliability Engineering best practices and Platforms/DevOps best practices. Exposure to AI concepts such as LLMs, RAG, and agent‐based systems (or a strong desire to learn). Experience previously working with central government and an understanding of government ways of working ...

Comfortable using modern AI-assisted engineering workflows, including agentic coding tools and LLM-based automation. Familiarity with concepts such as MCP servers, tool-calling, RAG/vector search, embeddings, and AI-assisted platform operations. Working knowledge of databases and data platforms, including SQL databases such as PostgreSQL/MySQL ...

looking for an AI Engineer to join their London-based team and design next-generation intelligent systems powered by RAG (Retrieval-Augmented Generation) and agentic AI architectures . If you thrive at the intersection of software engineering and applied AI, this … your chance to make a real impact. 💡 What You’ll Do Design and deploy RAG pipelines that turn data into actionable intelligence Build agentic AI systems capable of reasoning, planning, and tool use Collaborate with product and engineering teams to bring AI features into production Optimize performance, scalability, and reliability ...

Ready Data Architecture Architect data stores optimised for large language model (LLM) consumption, including vector databases, embedding pipelines, and retrieval‐augmented generation (RAG)‐compatible data structures that form the foundation of the AI product layer. Data Contracts & Platform Interfaces Partner with Business Analysts … cloud connectivity. AI & ML Enablement Practical experience designing data infrastructure to support AI/ML workloads, including vector stores, embedding pipelines, and integration with RAG‐based systems. Stakeholder & Communication Skills Excellent interpersonal skills, with the ability to explain complex data architecture concepts clearly to Business Analysts and non‐technical stakeholders. ...

technical delivery — from requirements gathering through to deployment and ongoing support Hands-on experience with AI/LLM integration (OpenAI, Azure OpenAI), RAG pipelines, or agentic frameworks is a key requirement Will provide technical leadership and mentoring to junior engineers and actively participate in architecture discussions and code reviews Must ...

Proven experience leading AI, machine learning, or intelligent automation initiatives in a mid-to-large organisation Strong hands-on knowledge of generative AI (LLMs, RAG architectures) and/or agentic AI frameworks Experience building or scaling AI/data science teams Track record of delivering AI solutions into production environments ...

Microsoft AI stack: Azure OpenAI Service, Azure AI Foundry, Semantic Kernel, Copilot Studio, Power Platform, and Microsoft 365. Strong understanding of LLM architectures, RAG patterns, agentic workflows, and prompt engineering. Experience delivering complex AI transformation programmes for clients in regulated industries. Demonstrated ability to translate technical AI capabilities into compelling ...

ability to shape enterprise-level software or AI solutions AI & GenAI Expertise Understanding of LLMs, Generative AI, and model architectures Familiarity with concepts like RAG, fine-tuning, model selection, and performance trade-offs Ability to assess use case viability and business value Cloud & Technology Experience across at least ...

Familiarity with AI-assisted development tools such as GitHub Copilot, Claude, and Cursor. Azure AI Engineer certification or equivalent practical experience. Understanding of MCP, RAG architectures, or vector databases. Benefits Industry-leading training and development opportunities. Competitive salary with progression potential. 23-days holiday allowance, increasing with length of service ...

Experience: Proven track record of leading the delivery and maintenance of complex software products in a DevOps and Agile environment. Deep practical experience with RAG, LLMs, and MLOps. Expert‐level understanding of NLP, Embeddings, and Prompt Engineering. Expert proficiency in Python, React, and TypeScript. Significant experience with Cloud platforms ...

models (DAX and Power Query) Exposure to ML/AI concepts (e.g. scikit-learn, TensorFlow) or an interest in preparing data for LLMs, RAG architectures or predictive models Experience with Docker and/or Terraform (desirable) What You Will Learn & What Liquidline Can Offer You Being a part of Liquidline ...

Building and deploying LLM-powered systems into production • AI agents, orchestration, multi-step reasoning, and tool usage • LangChain/LangGraph or similar frameworks • RAG pipelines and retrieval systems • MCP-style architectures/FastAPI/orchestration patterns • Production-grade Python development • Cloud deployment experience (AWS/Azure/ ...

integrating AI/ML features via APIs (e.g. OpenAI, Anthropic or similar) Exposure to data pipelines, inference workflows, or retrieval-based systems (RAG, vector DBs or similar) Cloud & DevOps: AWS, Azure, or GCP Docker, CI/CD pipelines (GitHub Actions, GitLab CI or similar) Experience deploying and maintaining ...

production environments — not just prototypes. Key experience required: • Python and production-grade coding • LLMs, AI Agents & Agentic Workflows • LangChain/LangGraph or similar frameworks • RAG Systems • MCP/FastMCP/FastAPI architectures • Cloud platforms (AWS, Azure, or GCP) • AI system deployment, orchestration & optimisation. This role offers the opportunity to work ...

EventBridge, SQS, Step Functions Databases: DynamoDB, PostgreSQL, Aurora, MongoDB Frontend: React, TypeScript, MUI DevOps: AWS CDK, Terraform, Docker, GitLab CI, CodePipeline AI: Amazon Bedrock, RAG, LLM integrations Skills Required Commercial software engineering experience Strong AWS cloud/serverless experience Strong backend development skills in TypeScript, Python, or Go CI/ ...

premises, cloud, and hybrid AI solutions. GenAI Leadership: Deploy and customize a variety of generative models, leveraging Azure OpenAI, Vector Databases, and RAG patterns. Technical Governance: Managed and governed technical teams to ensure best practices in AI data engineering. Stakeholder Management: Act as the primary technical point of contact, gaining ...

premises, cloud, and hybrid AI solutions. GenAI Leadership: Deploy and customize a variety of generative models, leveraging Azure OpenAI, Vector Databases, and RAG patterns. Technical Governance: Managed and governed technical teams to ensure best practices in AI data engineering. Stakeholder Management: Act as the primary technical point of contact, gaining ...

prioritise AI opportunities across the organisation Required ExperienceEssential Deep expertise in Python and relevant AI/ML frameworks and SDKs Proven experience building RAG pipelines that operate effectively in production environments Hands-on experience with model packaging, deployment, and production AI workflows Strong understanding of enterprise infrastructure concepts including VNets ...

designing and optimising multi-agent systems for reliability and production performance Understanding of prompt engineering techniques including few-shot learning, chain-of-thought and RAG Familiarity with LLM evaluation strategies and how to measure whether your system is actually working Hands-on with AI orchestration frameworks: LangGraph, LangChain, DSPy ...

minimum of 10 years of experience in software engineering with a proven track record in a DevSecOps and Agile environment. Deep practical experience with RAG, LLMs, and MLOps, alongside expert proficiency in Python, React, and TypeScript. What will give you a competitive edge (preferred qualifications): Expert-level understanding ...

minimum of 10 years of experience in software engineering with a proven track record in a DevSecOps and Agile environment.* Deep practical experience with RAG, LLMs, and MLOps, alongside expert proficiency in Python, React, and TypeScript.**What will give you a competitive edge (preferred qualifications):*** Expert-level understanding ...

leveraging Azure AI Foundry as the pro-code platform - including multi-agent orchestration, tool integration (MCP, A2A, OpenAPI), agent memory, and enterprise knowledge grounding (RAG)* Assess opportunities for Intelligent Automation based on technical feasibility and business value, to enable prioritisation of the backlog* Evaluate opportunities against alternative internal solution options ...

activities. Working within an AI Centre of Enablement, you will provide deep technical expertise across Generative AI, Agentic AI, Retrieval Augmented Generation (RAG), and enterprise AI delivery. This role requires a strong hands-on technical background combined with the ability to guide best practices … support enterprise-scale AI adoption. Key Responsibilities * Design and develop advanced AI solutions using LLMs, RAG architectures, and automation workflows. * Deliver scalable AI capabilities across enterprise environments within the Microsoft AI ecosystem. * Support AI model optimisation, data readiness, and performance enhancement activities. * Develop reusable AI assets including prompt libraries, templates ...