376 to 400 of 884 Retrieval-Augmented Generation Jobs in England

partnership with cross‐functional teams Preferred Qualifications, Capabilities, and Skills Experience with NLP and generative AI, including large language models, retrieval‐augmented generation, tool and function calling, agentic workflows, multi‐agent orchestration, and related evaluation and safety patterns Familiarity with agentic building blocks ...

data pipelines and platform components Experience of coaching and mentoring data engineers to achieve successful outcomes Familiarity with AI-focused data engineering, including RAG systems, vector databases, embedding pipelines, and semantic indexing A proven record of collaboration with colleagues and stakeholders Process mapping and supporting documentation production Test plan formulation ...

Migration of analytical/data science workloads (convert and migrate R development/Libraries to Databricks). AI/ML, LLM integration, model lifecycle, RAG/vector retrieval or model-serving experience. Large-scale enterprise platform transformation and regulated-industry experience. Strong cost optimisation and FinOps delivery experience ...

platform. This hands-on leadership role focuses on scalable engineering patterns, accelerating delivery, and mentoring development teams. You will design and oversee LLM, RAG, and Agentic AI integrations across Azure, AWS, and GCP, collaborate with architects to ensure secure, maintainable solutions, and set standards for observability, governance, and #J ...

track record delivering enterprise-scale AI/ML solutions into production Strong experience leading technical teams and complex transformation programmes Deep expertise in LLMs, RAG architectures, Prompt Engineering and Generative AI frameworks such as LangChain or LangGraph Hands-on experience with Python, PyTorch, TensorFlow, MLOps/LLMOps and cloud platforms ...

track record delivering enterprise-scale AI/ML solutions into production Strong experience leading technical teams and complex transformation programmes Deep expertise in LLMs, RAG architectures, Prompt Engineering and Generative AI frameworks such as LangChain or LangGraph Hands‐on experience with Python, PyTorch, TensorFlow, MLOps/LLMOps and cloud platforms ...

track record delivering enterprise-scale AI/ML solutions into production Strong experience leading technical teams and complex transformation programmes Deep expertise in LLMs, RAG architectures, Prompt Engineering and Generative AI frameworks such as LangChain or LangGraph Hands-on experience with Python, PyTorch, TensorFlow, MLOps/LLMOps and cloud platforms ...

track record delivering enterprise-scale AI/ML solutions into production Strong experience leading technical teams and complex transformation programmes Deep expertise in LLMs, RAG architectures, Prompt Engineering and Generative AI frameworks such as LangChain or LangGraph Hands-on experience with Python, PyTorch, TensorFlow, MLOps/LLMOps and cloud platforms ...

willingness to work with or learn GoHands-on experience building and operating production AI applications using large language models and agentic frameworksUnderstanding of RAG, MCP, LangGraph or similar technologies, including tool integration and prompt designExperience creating reliable AI agents through evaluation, guardrails and token and cost optimisationProficiency with Docker, Kubernetes ...

work with or learn Go Hands-on experience building and operating production AI applications using large language models and agentic frameworks Understanding of RAG, MCP, LangGraph or similar technologies, including tool integration and prompt design Experience creating reliable AI agents through evaluation, guardrails and token and cost optimisation Proficiency with ...

integrating Generative AI/LLM functionality directly into software products Working with technologies such as OpenAI, Azure OpenAI, Claude, Gemini or local models Exploring RAG, AI agents, Semantic Kernel, MCP, vector search and intelligent workflows Engineering around the realities of production AI: accuracy, security, validation, guardrails, latency and cost Working ...

integrating Generative AI/LLM functionality directly into software products Working with technologies such as OpenAI, Azure OpenAI, Claude, Gemini or local models Exploring RAG, AI agents, Semantic Kernel, MCP, vector search and intelligent workflows Engineering around the realities of production AI: accuracy, security, validation, guardrails, latency and cost Working ...

integrating Generative AI/LLM functionality directly into software products Working with technologies such as OpenAI, Azure OpenAI, Claude, Gemini or local models Exploring RAG, AI agents, Semantic Kernel, MCP, vector search and intelligent workflows Engineering around the realities of production AI: accuracy, security, validation, guardrails, latency and cost Working ...

ExperienceSolution Architecture, Enterprise Architecture or AI Architecture experience.Strong understanding of Generative AI, Large Language Models (LLMs) and AI agent frameworks.Experience designing AI solutions using RAG, prompt engineering and knowledge-grounded architectures.Expertise in Microsoft AI technologies, including Microsoft 365 Copilot, Copilot Studio, Azure AI and/or Power Platform.Experience delivering solutions ...

Python and JavaScript Hands-on experience with backend frameworks and modern frontend frameworks Deep knowledge of Generative AI and agent-based AI frameworks including RAG and orchestration patterns Strong experience working with vector databases and AI-specific data stores Solid understanding of AI and ML models, LLMs and generative ...

system design, application development, testing, and operational stability. Experience integrating AI/ML techniques into software systems, including familiarity with LLMs, Generative AI, NLP, RAG, AI evals, and coding assistants. Managing and mentoring software engineering or AI/ML teams, with experience as a hands-on practitioner delivering production-grade ...

from early foundations to a production-grade, self-service platform: Amazon Bedrock and the surrounding tooling (model access and routing, guardrails, knowledge bases/RAG patterns, agentic workflows), with the observability, cost controls and security posture required to run it in a regulated environment (AI Experience not essential but highly ...

containers and cloud‐native delivery approachesCuriosity about AI‐enabled systems and how they scale responsiblyExperience with LLMs, AI agent orchestration, or tools such as RAG, vector databases or LangChain is a bonus, but not essential.What’s in it for youFlexible hybrid working25 days leave, 2 days paid for volunteering ...

from early foundations to a production-grade, self-service platform: Amazon Bedrock and the surrounding tooling (model access and routing, guardrails, knowledge bases/RAG patterns, agentic workflows), with the observability, cost controls and security posture required to run it in a regulated environment (AI Experience not essential but highly ...

data architectures, including multi-tenant systems, fine-grained authorization, identity and access management, API-first architectures, data lakehouse patterns, Delta Lake, vector databases, and RAG pipelines- Strong understanding of model-agnostic AI architectures and tool integration, including multi-provider LLM integration, tool calling, orchestration, and abstraction patterns- Exceptional technical ...

home 100% of the time.Your Role:GenAI Solution DevelopmentDesign, develop, and deploy enterprise-grade Generative AI solutions.Lead implementation of AI use cases leveraging RAG, Prompt Engineering, Open-Source LLMs, LangChain, and related frameworks.Ensure scalable, secure, and production-ready AI deployments.Project LeadershipDrive AI programs and initiatives across EMEA regions.Lead AI adoption ...

Practice eval‐driven development: Build and own the evaluation harnesses that measure accuracy, hallucination, and performance before anything ships. Solve performance at scale: Tackle RAG latency optimization, context‐window management, and intelligent information routing under real production load. Build observability: Ship tooling that gives the team real‐time insight into ...

roleProficiency in Python and working knowledge of AI/ML frameworks (e.g. OpenAI, Hugging Face, LangChain, Pinecone, Weaviate, etc.)Familiarity with building LLM applications, RAG systems, or conversational AI workflowsExperience integrating with enterprise systems via APIs or middleware (e.g. Salesforce, ServiceNow, SAP, Oracle)Ability to translate business problems into technical ...

practices, system design, application development, testing, and operational stability.Experience integrating AI/ML techniques into software systems, including familiarity with LLMs, Generative AI, NLP, RAG, AI evals, and coding assistants.Managing and mentoring software engineering or AI/ML teams, with experience as a hands-on practitioner delivering production-grade solutions.Demonstrated ...

managed customer relationships, navigated security reviews, and delivered production systems organisations depend on.Deep production LLM experience: multi-agent orchestration, MCP/tool-use, RAG optimisation, evaluation frameworks, agent deployment at scale.System architecture expertise spanning enterprise systems, cloud infrastructure, and AI services explainable to a CIO and a junior engineer.Demonstrated ability ...