226 to 250 of 252 Retrieval-Augmented Generation Jobs in England

consultative environment. Responsibilities Design end-to-end AI and Generative AI architectures for enterprise-scale transformation programmes Define solution architectures leveraging LLMs, RAG, vector databases and AI agent frameworks Work with senior client stakeholders to translate business challenges into scalable AI solutions and implementation roadmaps Design secure, responsible and scalable … experience designing and delivering enterprise AI, Machine Learning or Advanced Analytics solutions Strong understanding of modern AI technologies including Generative AI, LLMs, Prompt Engineering, RAG and Vector Databases Experience architecting production-grade AI platforms and applications Strong knowledge of at least one major cloud platform (Azure, AWS or GCP) Ability ...

work includes: Building and operating LSEG AI Assist, an in‐house agentic experience capable of reasoning, planning, and tool‐calling. Operating QAS, the enterprise RAG and search layer used to ground agent responses in approved data sources. Delivering a production Internal MCP Gateway providing discovery, security, policy enforcement, observability … understanding of Model Context Protocol (MCP), including server and tool patterns. FastAPI and REST API design and implementation experience. Experience with prompt engineering and RAG‐based architectures. Containerisation and Kubernetes‐based deployment experience. Ability to work across platform, product, and governance boundaries in an enterprise environment. Career Stage: Manager London ...

Agent Runtime Development: Build robust infrastructure for packaging, deploying, and managing agents in containers. RAG Integration: Enhance agent capabilities with Retrieval‐Augmented Generation systems to provide contextual knowledge and domain expertise. Evaluation & Testing: Design evaluation frameworks to measure agent performance, reliability, and containerized deployment … development. Proven ability to design scalable, distributed systems. Deep understanding of Docker, containerization best practices, and container orchestration. Experience building Retrieval‐Augmented Generation systems (secondary focus). Experience with AI orchestration frameworks such as CrewAI, AGNO, ADK, LangChain/LangGraph (preferred). Python proficiency ...

Engineer | Digital Agency | £60-70,000 NLP | LLM | RAG | AI | ML My client is currently looking for an AI Engineer , to play a key role in designing, building and embedding AI-enabled capabilities across digital products and shared platforms. You will focus on the practical application of large language models … experience building solutions using LLMs, machine learning or applied AI techniques to solve practical business problems. Experience with prompt engineering, retrieval augmented generation, semantic search, AI workflow orchestration or agent-based approaches. Experience working with business or domain data to ground, evaluate and improve ...

Doing Rapidly prototype, build and validate new AI-driven solutions, taking concepts from idea to working demo in days. Develop components using LLMs, RAG, prompt engineering and modern agent frameworks . Work closely with architects, product leaders and engineers to de-risk emerging technologies and shape future solutions. Leverage … Strong software engineering experience with C#/.NET Core, Angular/TypeScript and Python . Practical experience working with AI technologies including LLMs, RAG, agent frameworks and prompt engineering . A track record of rapidly building prototypes, MVPs and proof-of-concepts. Experience within insurance, reinsurance, risk analytics or financial ...

wealth management and financial services organisation is looking to appoint a Senior Azure AI Platform Engineer to help build and operationalise the next generation of AI-enabled services and platforms across the business. This is a hands-on engineering role focused on designing, building and supporting production-grade … operational ownership Desirable Experience MCP Servers Model Context Protocol Semantic Kernel Prompt Flow Agentic AI AI orchestration frameworks Copilot Studio Power Platform integrations RAG architectures AI governance and observability Technology Stack Azure AI Foundry | Azure OpenAI | C# | .NET | Azure Functions | Azure API Management | Azure Service Bus | Azure Logic Apps ...

reusable agent components and playbooks Proactively surface new automation opportunities across the business Key Skills Python & LLM agent frameworks (LangChain, CrewAI, LlamaIndex) Prompt engineering & RAG pipelines Business analysis & stakeholder management Process mapping & workshop facilitation Microsoft 365 & Microsoft Copilot No-code/low-code tooling (Zapier, Make) Desirable Skills Experience … etc. Strong enough technically to assess feasibility and scope what is buildable; commercially sharp enough to prioritise what is worth building. Familiarity with LLMs, RAG pipelines, tool/function calling, and integrating AI into real business workflows. Proven ability to lead discovery workshops and translate complex business problems into clear ...

reusable agent components and playbooks • Proactively surface new automation opportunities across the business Key Skills • Python & LLM agent frameworks (LangChain, CrewAI, LlamaIndex) • Prompt engineering & RAG pipelines • Business analysis & stakeholder management • Process mapping & workshop facilitation • Microsoft 365 & Microsoft Copilot No-code/low-code tooling (Zapier, Make) Desirable Skills • Experience … etc. • Strong enough technically to assess feasibility and scope what is buildable; commercially sharp enough to prioritise what is worth building. • Familiarity with LLMs, RAG pipelines, tool/function calling, and integrating AI into real business workflows. • Proven ability to lead discovery workshops and translate complex business problems into clear ...

Senior AI/ML Engineer (LLM's/RAG Pipeline Development)Guildford, Surrey (Fully Onsite)£85000 - £125,000 + 25% Bonus, 10% Pension, Private Medical.- Must have a Masters or PhD Degree in a STEM/Computing discipline.- Must work full-time, 5 days a week in the Guildford office. ...

data platform. You will design test strategies, validate AI models, and build automated quality gates within complex cloud and data pipelines. Key Responsibilities AI & RAG Validation: Define evaluation frameworks to validate GPT responses, summarisation accuracy, and RAG performance. Automation & CI/CD: Build and maintain automated test frameworks integrated into ...

platform. You will own ML components end to end — from research and prototyping through to production deployment — working across knowledge bases, vector stores, RAG pipelines, and context engines to power agents that deliver real impact for enterprise customers. You will: Own large areas of platform end to end, driving components … through to production deployment. Work on knowledge representation systems, including ontologies and knowledge graphs, to support structured reasoning over enterprise data. Design and implement RAG pipelines, including chunking, embedding, indexing, retrieval, and reranking. Build and maintain integrations between retrieval and ML components and diverse enterprise data ...

that includes conversational, Agentic AI's. This role focuses on building advanced conversational AI systems, implementation AI Agents across different use cases, implementing sophisticated RAG architectures, and deploying production-grade voice AI applications. This is a newly created role in a rapidly growing business. A brilliant opportunity! The following skills …/experience is essential: Hands-on experience with advanced RAG systems Strong experience with LLM technologies Agentic AI experience Software Engineering background Good communication skills Salary: Up to £95,000 + bonus + package Location: London (good work from home options available) If you are interested in this Senior ...

into scalable products. What you’ll be doing Build and deploy production AI solutions using Python and modern AI frameworks Develop LLM applications using RAG, prompt engineering and agentic workflows Shape AI architecture, tooling and engineering standards Work across Azure, cloud infrastructure and deployment pipelines Integrate AI solutions into existing … engineers and support technical decision-making What we’re looking for Strong software engineering background with production AI experience Python experience Experience with LLMs, RAG and AI application development Azure and cloud infrastructure experience Understanding of APIs, DevOps and scalable systems Experience influencing technical direction and mentoring engineers This ...

reusable platform services and enabling technologies across Azure (e.g. APIM, Azure AI Foundry, Databricks, MCP) that accelerate delivery of AI solutions such as Agents, RAG, and LLM‐powered applications. You will work closely with architecture, AI engineering, and product teams to translate business needs into robust, platform‐first solutions, ensuring … codified engineering practices. What You Will Do Design and build scalable platform capabilities across Azure to support AI use cases (Agents, Agentic workflows, RAG, LLMs), including APIs, integrations, and model serving layers. Produce high‐quality technical designs and guide implementation, ensuring solutions are secure, maintainable, and aligned to architectural standards. ...

when supporting clients in a Consulting capacity, and have a background as a strategic consultant.Translate AI into business value – Turn advanced capabilities (GenAI, LLMs, RAG, copilots, agentic systems) into clear, actionable strategiesEngage and influence executives – Build trusted relationships with senior and C-suite stakeholders to shape decisions and outcomesDrive commercial … Services) as a strategic consultant who has delivered several project life cycles.Technical Knowledge - Hands-on or applied experience with:Generative AI (LLMs, prompt engineering, RAG, copilots, multimodal AI)Agentic AI (autonomous agents, multi-agent workflows, tool integration)Broad strategic knowledgeScaling AI Experience - Experience designing and implementing enterprise AI operating models ...

passionate about shaping how organisations deploy and scale AI? I'm supporting a leading business in the Insurance Sector as they build next-generation AI and integration capabilities across their enterprise. You'll work at the intersection of GenAI engineering, cloud architecture, and enterprise integration, helping define … What you'll be doing Designing and deploying AI-enabled solutions across cloud environments. Leading on GenAI orchestration (LangChain, Autogen, Google ADK). Architecting RAG pipelines, vector database integrations, and prompt management strategies. Building guardrail frameworks to prevent hallucinations, injection and bias. Designing API-first and event-driven architectures. Influencing ...

various AI providers Tool Creation: Developing "Tools" (Function Calling) that allow LLMs to interact with real-world data, enabling features such as consultant lookups (RAG) and clinical system integrations Data Insights: Building pipelines to transcribe and analyse historical call recordings to extract sentiment, summary, and outcome data Prompt Engineering: Refining … understanding of JSON data structures AI/LLM Exposure: Practical experience interacting with LLM APIs (OpenAI, Anthropic, etc.) and understanding concepts like Context Windows, RAG, System Prompts, and Temperature Concurrency: Understanding of asynchronous programming (WebSockets, goroutines) is highly beneficial given the real-time nature of voice data Several years experience ...

will be the face of Databricks at relevant events, building strategic relationships with VCs, accelerators, and incubators to identify and influence the next generation of "unicorns." Strategic Technical Discovery: Move beyond traditional "hands‐on" SA work to focus on high‐level architecture and strategic road‐mapping. You will … business decision‐makers. Nice to have: Deep expertise in articulating the strategic and commercial value of Generative AI , including Large Language Models (LLMs), RAG architectures, and the shift toward Compound AI Systems . Familiarity with the latest advancements in Agentic Workflows , model fine‐tuning (Mosaic AI), and efficient inference ...

party platforms Develop microservices, wrappers, connectors, and service interfaces to support solution components Support applied AI development including LLM services, speech services, classification models, RAG pipelines, and AI-enabled application features Prepare and maintain demo-ready technical builds for internal and customer-facing demonstrations Support the preparation of technical handoff … environment Excellent communication skills to articulate technical issues and blockers clearly Desirable Skills: Experience with AI/ML tools, LLMs, speech services, RAG systems, or AI APIs Experience with Unreal Engine, Unity, or other real-time 3D development environments Familiarity with Docker, FastAPI, REST APIs, Git, and deployment workflows Experience ...

data‐intensive, fast‐moving engineering org — and has the range to show for it Can credibly walk into a conversation about data pipeline architecture, RAG evaluation, API design, or system scalability — and add something useful in all of them Owns problems, not just opinions. You're not here to review … Deep experience with complex data modelling, entity resolution, and data pipelines in production environments Proven experience designing and shipping AI/LLM systems — RAG, structured extraction, evaluation frameworks — beyond the prototype stage Strong backend and distributed systems knowledge: APIs, service architecture, scalability, observability Experience making and owning foundational architectural decisions ...

autonomy to build and shape the AI function as your own. What You'll Do Lead the design and deployment of a market-leading RAG conversational agent, working with a large, complex proprietary dataset. Partner with product managers, content managers and senior stakeholders to design, develop, deploy and optimise state … user requirements. Optimise LLMs, datasets and agent workflows to the highest quality standards, detecting and mitigating hallucinations and output errors. Deploy and maintain RAG architecture and LLM pipelines within cloud environments. Optimise data-chunking and text embeddings for optimal semantic search across a large, specialist corpus. Track the competitive landscape ...

deployments, including hands-on technical experience designing, configuring, and delivering AI solutions that leverage LLMs (e.g., prompt and workflow design/orchestration, retrieval-augmented generation, usage tracking, automated testing, evaluation and monitoring), including tailored agentic and agent-to-agent patterns. Demonstrated ability to engage ...

operate hands‐on where needed. Proven experience leading teams of Principal Engineers and Architects in complex environments. Strong understanding of AI integration patterns (e.g. RAG, embeddings, vector databases), cloud‐native architectures, and scalable distributed systems. Experience overseeing AI‐enabled product capabilities at scale. Proven ability to drive high standards … software architecture patterns, including microservices, event‐driven systems, and cloud‐native design. Strong understanding of AI/ML concepts and integration approaches, including LLMs, RAG architectures, vector databases, and responsible AI practices. Expertise in scalable platform design, performance optimisation, and distributed systems used by high‐volume, global user bases. Solid ...

solution concepts into actionable architecture, design and delivery guidance for engineering teams. Design and implement AI-powered assistants, agents and intelligent workflow solutions leveraging RAG and advanced orchestration techniques. Evaluate emerging AI technologies, frameworks and industry trends to drive innovation and best practice. Provide technical leadership, mentoring and guidance … strongly preferred (e.g., Bedrock, Lambda, Step Functions and related AI services). Hands-on experience implementing AI agents, multi-agent systems, intelligent workflow automation, RAG architectures and AI-powered assistants in enterprise environments. Strong knowledge of agentic frameworks and orchestration technologies such as LangGraph, AutoGen, CrewAI, Agent Development ...

needs. Work closely with engineering to build, test, and refine products. Ensure AI customer service agents are adaptive and intelligent, leveraging LLMs, reinforcement learning, RAG techniques, and expert feedback. Monitor and measure performance, using data‐driven insights to refine AI responses, minimize hallucinations, reduce handling times, and enhance overall client … plus: experience in scaling AI‐driven customer service solutions, including chatbot and voice agents. Deep understanding of LLMs, speech‐to‐speech technologies, RAG and AI‐driven automation, including how to adapt AI models for customer support applications. Proven experience leading cross‐functional teams, including engineering and data science. Strong analytical ...