576 to 600 of 876 Retrieval-Augmented Generation Jobs in the UK

squad's needs evolve. Requirements: Engineering experience: 3+ years in software engineering, with hands-on experience building LLM-powered applications in production (RAG, tool-augmented agents, or agentic workflows). Education: BS in Engineering, Computer Science, or equivalent. Ways of working: Comfortable working autonomously within a defined problem … doesn't make sense, and delivering in short, time-boxed cycles where validated outcomes matter more than perfect solutions. Agentic AI/LLMs: Building RAG pipelines, tool-augmented agents, and agentic workflows; familiar with prompt engineering, context management, evaluation, and observability. Agent fundamentals: Understands how agents use memory ...

doesn't make sense, and delivering in short, time-boxed cycles where validated outcomes matter more than perfect solutions. Agentic AI/LLMs: Building RAG pipelines, tool-augmented agents and agentic workflows; familiar with prompt engineering, context management, evaluation and observability. Agent fundamentals: Understands how agents use memory … information to universities. We help innovators turn knowledge into new products. We help health professionals improve patient care and educators train the next generation of doctors and nurses. Connecting quality content and innovative technologies, we make progress go further and happen faster. And by championing inclusion and sustainability ...

that reached real users. Worked with transformer architectures or fine-tuned language models. Designed evaluation frameworks for generative AI systems. Built retrieval, RAG, embedding or semantic-search systems. Worked with large, noisy or multilingual text datasets. Built high-throughput or low-latency data pipelines. Experience operating machine learning … would be valuable, but we do not expect you to know everything: PyTorch, TensorFlow or JAX Hugging Face LLM APIs and open-source models RAG and vector databases Apache Airflow AWS S3 ECS/EKS Redshift Docker Kubernetes GitHub Actions Pulumi or Terraform SQL Model monitoring and observability Distributed processing ...

production in an enterprise context (beyond prototypes or sandbox experiments). AI architecture for experience platforms: system design for intelligent products and services, including RAG, GraphRAG, agent orchestration and proximity/relevance evaluation. Hands‐on implementation with Vercel AI SDK, Vercel AI Cloud and agentic cloud patterns for production … analysis, observability, governance, testing and evaluation frameworks for production systems. Practical application of Generative AI to marketing and experience challenges; e.g. personalisation, content generation, campaign optimisation; with measurable production outcomes. Design Systems and Design to Code Design to Code workflow: advanced Design Systems thinking; experience bridging design intent ...

Generative AI, Agentic AI, and advanced data science solutions for real-world business problems. You'll design and develop multi-agent systems, implement RAG pipelines, and deliver production-ready AI applications that transform client capabilities across multiple industries. If you're passionate about pushing the frontiers of LLMs, orchestration frameworks … frameworks such as LangGraph, CrewAI, AutoGen, and Semantic KernelImplement orchestration patterns including planner/executor, supervisor/worker, and tool-calling workflowsDesign and build RAG pipelines, including embeddings, chunking, hybrid search, and retrieval evaluation for enterprise data groundingDevelop orchestration engines supporting multi-step planning, delegation, and fallback paths ...

efficiency ; shorter cycle times, less manual effort, higher accuracy, lower cost-to-serve. Engineer the agent core: apply prompt engineering, context engineering, prompt caching, RAG/context-graph retrieval, memory, tool/function calling, MCP integration and multi-agent orchestration. Integrate to standards: connect agents into client ecosystems … Agents SDK, AWS Bedrock AgentCore or Microsoft Agent Framework/Semantic Kernel). Solid command of agent engineering: prompt engineering, context engineering, prompt caching, RAG/context graphs, tool/function calling, MCP, and multi-agent orchestration. Eval-driven development: designing evaluation harnesses and measuring agent quality, safety and reliability. ...

efficiency ; shorter cycle times, less manual effort, higher accuracy, lower cost-to-serve. Engineer the agent core: apply prompt engineering, context engineering, prompt caching, RAG/context-graph retrieval, memory, tool/function calling, MCP integration and multi-agent orchestration. Integrate to standards: connect agents into client ecosystems … Agents SDK, AWS Bedrock AgentCore or Microsoft Agent Framework/Semantic Kernel). Solid command of agent engineering: prompt engineering, context engineering, prompt caching, RAG/context graphs, tool/function calling, MCP, and multi-agent orchestration. Eval-driven development: designing evaluation harnesses and measuring agent quality, safety and reliability. ...

world enterprise use cases, driving POCs and transformation initiatives from concept to production. What You'll Do Design and build GenAI solutions using LLMs, RAG and prompt engineering Develop Agentic AI systems for automation and orchestration Build and deploy ML models for business use cases Work across AWS services including … years hands-on in AI/ML and GenAI Strong Python and Java skills Experience with LangChain, LlamaIndex, CrewAI or similar frameworks Knowledge of RAG, vector databases and semantic search AWS expertise (Bedrock and SageMaker preferred) Experience with APIs, microservices and cloud-native architecture Nice to Have AWS certifications ...

evaluation, and multi-agent patterns, and you understand the trade-offs involved. Experience with RAG systems – You have a solid understanding of retrieval-augmented generation systems and pipelines, and you've built them in real-world contexts. Cloud platform experience – You're experienced with … doIn this role, you will: Design and build cutting-edge AI applications – from MCP servers and voice-to-voice real-time apps to RAG bots and multi-agent systems. You'll work on technology that's genuinely new territory. Advise clients on AI strategy – join workshops, contribute to proposals ...

solution delivery: Design and build AI-enabled applications, copilots, agents, extraction pipelines, prediction interfaces, and decision-support tools using foundation models, retrieval-augmented generation, structured outputs, and orchestration frameworks. Forward deployed problem solving : Work directly with business teams, product owners, clients, or operational users … Protocol (MCP), and enterprise-scale automation use cases. Hands-on experience building AI-enabled systems, such as LLM pipelines, document extraction, structured output generation, AI-assisted analytics, prediction interfaces, or agentic workflows. Experience working with business-critical systems where reliability, maintainability, operational support, and measurable business impact ...

including typed data models, APIs, automated testing and maintainable system design. Hands-on experience with hosted LLM APIs, structured outputs, tool use, retrieval-augmented generation, context management and model limitations. Experience evaluating non-deterministic systems using representative datasets, explicit metrics, failure analysis and regression ...

Experience designing and operating APIs, microservices, distributed systems, and event-driven architectures at enterprise scale. Exposure to Generative AI, large language models, retrieval-augmented generation, semantic search, embeddings, knowledge retrieval, or agentic workflow patterns. Strong focus on software quality, including automated testing ...

vLLM and llm-d or directly equivalent model serving stacks in production Experience developing generative AI applications, AI agents, vector search, and retrieval-augmented generation patterns Experience building AI agents using orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar platforms Experience operating ...

surface it in usable, performant ways for brokers and analysts.* **AI-Enabled Development:** Build AI-enabled product features using patterns such as retrieval-augmented generation, tool and API integration, evaluation and human-in-the-loop design.* **AI-Assisted Engineering:** Use AI-assisted engineering tools ...

technology. You will combine strong business analysis and stakeholder engagement skills with practical AI solution experience across Large Language Models, Retrieval Augmented Generation, context engineering, prompt-as-code, evaluation frameworks, workflow automation and agentic patterns where appropriate. Working alongside clients, architects, engineers, data specialists … cases, shaping recommendations and influencing client decision-making. Strong understanding of modern AI technologies and patterns including Large Language Models, Retrieval Augmented Generation, AI agents, evaluation frameworks and model orchestration patterns. Practical experience applying techniques including context engineering, prompt-as-code and versioning practices ...

C# .NET (primary development language) AI coding tools: GitHub Copilot, Cursor or equivalents Prompt engineering and context design patterns Retrieval‐augmented generation (RAG), vector search, embeddings (desirable) Evaluation frameworks and automated quality pipelines for AI outputs Technical and Professional Expertise Solid production experience with … improve quality. Daily fluency with AI coding tools such as GitHub Copilot, Cursor or equivalents. Preferred Qualifications and Experience Retrieval‐augmented generation, agentic workflows, tool use, vector search or embeddings in production settings. Experience with LLM APIs across more than one provider, with ...

language model APIs across multiple providersAI coding tools: GitHub Copilot, Cursor or equivalentsPrompt engineering and context design patternsRetrieval-augmented generation (RAG), vector search, embeddings (desirable)Evaluation frameworks and automated quality pipelines for AI outputsTechnical and Professional ExpertiseSolid production experience with C# .NET, including working in established … improve quality. Daily fluency with AI coding tools such as GitHub Copilot, Cursor or equivalents. Preferred Qualifications and ExperienceRetrieval-augmented generation, agentic workflows, tool use, vector search or embeddings in production settings. Experience with LLM APIs across more than one provider, with a feel for their ...

language model APIs across multiple providersAI coding tools: GitHub Copilot, Cursor or equivalentsPrompt engineering and context design patternsRetrieval-augmented generation (RAG), vector search, embeddings (desirable)Evaluation frameworks and automated quality pipelines for AI outputsTechnical and Professional ExpertiseSolid production experience with C# .NET, including working in established … improve quality. Daily fluency with AI coding tools such as GitHub Copilot, Cursor or equivalents. Preferred Qualifications and ExperienceRetrieval-augmented generation, agentic workflows, tool use, vector search or embeddings in production settings. Experience with LLM APIs across more than one provider, with a feel for their ...

improve Design and ship multi-agent systems and production-grade automations that measurably improve how internal teams and customer-facing staff work Build RAG pipelines for internal knowledge retrieval that ground agents in real organisational context Keep the AI tooling stack healthy as usage scales across 2,500+ … development Hands-on experience building multi-agent systems using agentic frameworks — LangChain, LangGraph, MCP, Google ADK or similar Experience with RAG pipelines — retrieval-augmented generation for internal knowledge and workflow automation Background in enterprise tooling — rapid prototyping tools (Vercel/Railway), identity automation (Entra ...

improve Design and ship multi-agent systems and production-grade automations that measurably improve how internal teams and customer-facing staff work Build RAG pipelines for internal knowledge retrieval that ground agents in real organisational context Keep the AI tooling stack healthy as usage scales across 2,500+ … development Hands-on experience building multi-agent systems using agentic frameworks — LangChain, LangGraph, MCP, Google ADK or similar Experience with RAG pipelines — retrieval-augmented generation for internal knowledge and workflow automation Background in enterprise tooling — rapid prototyping tools (Vercel/Railway), identity automation (Entra ...

improve Design and ship multi-agent systems and production-grade automations that measurably improve how internal teams and customer-facing staff work Build RAG pipelines for internal knowledge retrieval that ground agents in real organisational context Keep the AI tooling stack healthy as usage scales across 2,500+ … development Hands-on experience building multi-agent systems using agentic frameworks — LangChain, LangGraph, MCP, Google ADK or similar Experience with RAG pipelines — retrieval-augmented generation for internal knowledge and workflow automation Background in enterprise tooling — rapid prototyping tools (Vercel/Railway), identity automation (Entra ...

improve Design and ship multi-agent systems and production-grade automations that measurably improve how internal teams and customer-facing staff work Build RAG pipelines for internal knowledge retrieval that ground agents in real organisational context Keep the AI tooling stack healthy as usage scales across 2,500+ … development Hands-on experience building multi-agent systems using agentic frameworks — LangChain, LangGraph, MCP, Google ADK or similar Experience with RAG pipelines — retrieval-augmented generation for internal knowledge and workflow automation Background in enterprise tooling — rapid prototyping tools (Vercel/Railway), identity automation (Entra ...

improve Design and ship multi-agent systems and production-grade automations that measurably improve how internal teams and customer-facing staff work Build RAG pipelines for internal knowledge retrieval that ground agents in real organisational context Keep the AI tooling stack healthy as usage scales across 2,500+ … development Hands-on experience building multi-agent systems using agentic frameworks — LangChain, LangGraph, MCP, Google ADK or similar Experience with RAG pipelines — retrieval-augmented generation for internal knowledge and workflow automation Background in enterprise tooling — rapid prototyping tools (Vercel/Railway), identity automation (Entra ...

Language Models (LLMs), AI-assisted development tools, and modern AI frameworks. Has understanding of AI system architecture, prompt engineering, vector databases, RAG (Retrieval-Augmented Generation), and AI service integration and has experience evaluating Has knowledge of database systems and excellent problem-solving skills ...

experience across some or all of the following: Generative AI, Large Language Models (LLMs), AI assistants and AI agents. Retrieval Augmented Generation (RAG), enterprise search and knowledge management solutions. Solution, platform or enterprise architecture. Data architecture, integration, APIs and governance. Security, compliance, risk management ...