176 to 200 of 304 Retrieval-Augmented Generation Jobs in the UK

alongside custom code, and the judgement to know when each is right. • AI & LLM engineering - model APIs (e.g. Anthropic, OpenAI), retrieval/RAG, embeddings and vector stores, agents and prompt design, integrated responsibly. • Cloud & delivery - hosting, serverless, containers, CI/CD and monitoring. • Data - modelling, storage, syncing ...

. AWS Bedrock Stack: Hands-on experience with Agents, Knowledge Bases, Guardrails, and model lifecycles. AI/ML Core: Mastery of FMs, RAG, tool-use, and non-deterministic agent logic. Security Frameworks: Proficiency in OWASP LLM Top 10 and NIST AI RMF . Role 1: Identity Expert Mission: Secure ...

SAFe, or hybrid) • Strong executive communication and stakeholder management skills Good to Have • Experience in enterprise AI or digital transformation programs • Familiarity with LLMOps, RAG, and AI observability concepts • Exposure to agent frameworks or AI platforms • Experience in Telecom, BFSI, or large enterprise environments How To Apply: It's easy ...

tooling. Strong numerical reasoning. Top-uni CS/maths/engineering/science, or equivalent in what you've built. Shipped production LLM systems — RAG, prompting, agents. The kind customers pay for. You've worked somewhere fast — startup or strong tech team — and make good calls on imperfect information. ...

multiple levels, from Engineers through to senior technical leadership. What you'll be doing Building AI-enabled platforms and intelligent workflows Working with LLMs, RAG pipelines and embedding models Developing scalable backend services and AI integrations Collaborating across engineering, architecture and business teams Supporting AI deployment, governance and operational rollout … Environment Python, SQL LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases, FastAPI, APIs AWS, Azure, GCP or Databricks CI/CD, MLOps and LLMOps Requirements Strong software or data engineering foundations Experience delivering AI or AI-enabled solutions Exposure to cloud and modern data platforms Financial ...

multiple levels, from Engineers through to senior technical leadership. What you'll be doing Building AI-enabled platforms and intelligent workflows Working with LLMs, RAG pipelines and embedding models Developing scalable backend services and AI integrations Collaborating across engineering, architecture and business teams Supporting AI deployment, governance and operational rollout … Environment Python, SQL LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases, FastAPI, APIs AWS, Azure, GCP or Databricks CI/CD, MLOps and LLMOps Requirements Strong software or data engineering foundations Experience delivering AI or AI-enabled solutions Exposure to cloud and modern data platforms Financial ...

multiple levels, from Engineers through to senior technical leadership. What you'll be doing Building AI-enabled platforms and intelligent workflows Working with LLMs, RAG pipelines and embedding models Developing scalable backend services and AI integrations Collaborating across engineering, architecture and business teams Supporting AI deployment, governance and operational rollout … Environment Python, SQL LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases, FastAPI, APIs AWS, Azure, GCP or Databricks CI/CD, MLOps and LLMOps Requirements Strong software or data engineering foundations Experience delivering AI or AI-enabled solutions Exposure to cloud and modern data platforms Financial ...

backend engineering, and cloud systems , designing and deploying intelligent features that directly impact users. Expect to: Design and implement LLM-based applications (RAG pipelines, agents, copilots) Build scalable backend services using Python and Java Integrate AI into existing cloud-native SaaS platforms Own the full lifecycle: experimentation prototyping production Optimise … software engineering, with strong experience in Python and/or Java Hands-on experience working with LLMs (prompt engineering, fine-tuning, embeddings, vector databases, RAG) Experience building and deploying applications in cloud environments (Azure preferred, AWS/GCP also considered) Strong understanding of APIs, microservices, and distributed systems Familiarity with ...

backend engineering, and cloud systems , designing and deploying intelligent features that directly impact users. Expect to: Design and implement LLM-based applications (RAG pipelines, agents, copilots) Build scalable backend services using Python and Java Integrate AI into existing cloud-native SaaS platforms Own the full lifecycle: experimentation prototyping production Optimise … software engineering, with strong experience in Python and/or Java Hands-on experience working with LLMs (prompt engineering, fine-tuning, embeddings, vector databases, RAG) Experience building and deploying applications in cloud environments (Azure preferred, AWS/GCP also considered) Strong understanding of APIs, microservices, and distributed systems Familiarity with ...

shipping production-grade AI systems, not producing slide decks about them. ────────────────────────────── WHAT YOU'LL BE DOING ────────────────────────────── 🔧 Technical Architecture • Designing end-to-end LLM and RAG systems integrated into complex enterprise workflows • Deploying and optimising models on AWS Bedrock, Azure AI Foundry, and self-hosted GPU infrastructure (vLLM, SGLang, Ollama) • Building … engineering excellence ────────────────────────────── WHAT WE'RE LOOKING FOR ────────────────────────────── ✅ Expert Python — plus hands-on experience with LangChain, LangGraph, PyTorch, TensorFlow or equivalent ✅ Deep knowledge of LLMs, RAG, prompt engineering, fine-tuning, vector databases, and generative architectures ✅ Production AI/ML delivery experience — real-world impact matters more than credentials ✅ Cloud-agnostic experience ...

Hands-on experience with LangGraph, LangChain, LlamaIndex, or CrewAI building multi-step agentic systems RAG & knowledge systems: Practical experience designing and building retrieval-augmented generation pipelines AI evals and observability: Experience building evaluation frameworks and using tools such as LangSmith to monitor and constrain ...

application Development: LLM fine-tuning adapting pretrained LLMs for specific tasks using techniques likeparameterefficient fine-tuning (PEFT) (e.g., LoRA, QLoRA). Implementing Retrieval-Augmented Generationpipelines to enhance the knowledge and accuracy of LLMs. Utilizing vector databases for efficient storage andretrieval of embeddings generated by LLMs. rafting ...

adaptive AI architectures Implementing prompt engineering at production scale Developing and managing multi-agent workflows Optimising retrieval systems and memory strategies (RAG architectures) Requirements 5+ years commercial experience with Python/PHP Proven experience delivering production-grade AI systems (not just prototypes) Hands-on experience building autonomous systems ...

with executive leadership on long-term growth strategy There’s a major focus on practical AI implementation, including: GenAI and automation AI-enabled operations RAG/knowledge systems workflow automation contact centre and admissions optimisation data and analytics architecture The environment includes: Salesforce CRM LMS/learning platforms cloud infrastructure ...

Python and FastAPI on the backend, React and TypeScript on the frontend, Postgres, ElasticSearch and vector databases for data, and LLMs, agents and RAG across the AI layer. What they're looking for Has built and managed an engineering team through a high-growth scaling phase, not just inherited ...

self-service tooling for model evaluation, prompt engineering, A/B testing, monitoring, and compliance validation Establish and evolve patterns for data pipeline management, RAG/retrieval design, model versioning, endpoint management, and agentic orchestration Develop comprehensive documentation, architectural guidance, runbooks, and examples that reduce onboarding time … technical audiences AI/ML & Ecosystem Knowledge Hands-on experience with LLMs, agentic frameworks, and GenAI tooling (models, APIs, orchestration platforms) Practical experience with RAG architectures, prompt engineering, fine-tuning workflows, and multi-agent systems Experience with SDLC acceleration using AI (e.g., copilot-style tools, automated testing, code generation ...

deliver solutions that create measurable commercial impact. What you'll be doing • Designing enterprise AI and GenAI architectures • Building scalable solutions across LLMs, RAG and Agentic AI • Defining AI platform, governance and deployment strategies • Advising senior stakeholders on AI adoption and transformation • Working across engineering, cloud, data and business teams … Driving AI from proof of concept to enterprise-scale delivery Technology Python, LLMs, RAG, Agentic AI LangChain, LangGraph Vector Search & Semantic Retrieval MLOps & LLMOps AWS, Azure, GCP, Databricks Kubernetes & Cloud-Native Platforms We're looking for • Experience designing enterprise AI, ML or data platforms • Strong cloud and modern ...

knowledge graph accurately and consistently Design and optimise Cypher queries for agent consumption, analytics and operational reporting Integrate the knowledge graph with LLM and RAG architectures to support AI agent knowledge retrieval Implement GraphRAG patterns to enable agents to traverse and reason over graph-structured knowledge Ensure graph … networkx, rdflib, py2neo) SQL Bash/Shell Scripting REST API integration GraphQL JSON-LD processing AI & Cloud Architecture AI Integration Cloud Platforms Governance & Operations RAG architecture design GraphRAG implementation Vector database integration LLM knowledge grounding Semantic retrieval design Agent knowledge API design AWS (Neptune, Glue, S3, Lambda) Azure ...

doing Designing AI and GenAI architecture strategies and roadmaps Building scalable AI/ML and Agentic AI platforms Working across LLMs, RAG, embeddings and semantic search Supporting AI deployment, governance and integration patterns Collaborating with engineering, data, DevOps and business teams Advising clients on AI platform capability and implementation strategy … Environment Python LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise AI or ML architectures Strong understanding of cloud and modern data platforms Exposure to scalable AI deployment ...

doing Designing AI and GenAI architecture strategies and roadmaps Building scalable AI/ML and Agentic AI platforms Working across LLMs, RAG, embeddings and semantic search Supporting AI deployment, governance and integration patterns Collaborating with engineering, data, DevOps and business teams Advising clients on AI platform capability and implementation strategy … Environment Python LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise AI or ML architectures Strong understanding of cloud and modern data platforms Exposure to scalable AI deployment ...

doing Designing AI and GenAI architecture strategies and roadmaps Building scalable AI/ML and Agentic AI platforms Working across LLMs, RAG, embeddings and semantic search Supporting AI deployment, governance and integration patterns Collaborating with engineering, data, DevOps and business teams Advising clients on AI platform capability and implementation strategy … Environment Python LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise AI or ML architectures Strong understanding of cloud and modern data platforms Exposure to scalable AI deployment ...

Role: AI Architect UK-based remote working Salary: Up to £125,000 (Bonus + Benifits Package) Lumora Solutions is representing a mission-driven software organisation seeking an AI Architect to join its growing AI Centre ...

experience with Docker, Linux/Unix CLI, Git, and testing Understanding of modern AI approaches, including: Agentic AI systems RAG (Retrieval Augmented Generation) Graph-based approaches Computer vision, audio, or multimodal models Knowledge of data governance and security best practices Strong communication and problem … solutions in secure environments High-performance computing and cloud-native AI architectures End-to-end MLOps lifecycle and model management Advanced AI techniques including RAG, agentic systems, and multimodal models Secure, high-assurance data environments Modern DevOps, CI/CD, and engineering practices Why Join? Work at the forefront ...

experience with Docker, Linux/Unix CLI, Git, and testing Understanding of modern AI approaches, including: Agentic AI systems RAG (Retrieval Augmented Generation) Graph-based approaches Computer vision, audio, or multimodal models Knowledge of data governance and security best practices Strong communication and problem … solutions in secure environments High-performance computing and cloud-native AI architectures End-to-end MLOps lifecycle and model management Advanced AI techniques including RAG, agentic systems, and multimodal models Secure, high-assurance data environments Modern DevOps, CI/CD, and engineering practices Why Join? Work at the forefront ...

years' experience building production-grade ML, data or software systems Proven experience deploying NLP or LLM-based features into production environments Strong exposure to RAG, retrieval or recommendation systems Experience working with vector databases and end-to-end LLM workflows Familiarity with graph databases and cloud data platforms ...