26 to 50 of 63 Retrieval-Augmented Generation Jobs in the UK excluding London

and leveraging both structured and unstructured data sources. Experimenting with the integration and fine-tuning of models with Vector databases and embeddings to support semantic search, RAG (retrieval-augmented generation), and domain-specific applications. Working within a Data Mesh architecture, collaborating across domains to ensure scalable, interoperable data products; containerising solutions with Docker and More ❯

and maintaining long-lived systems. Bonus Points for: Familiarity with financial services or private equity environments. Experience with Azure, Postgres, Streamlit, or similar tools. Practical experience with document-based RAG, Q&A, or fact extraction tasks. Thriving in small teams with fast feedback and iteration loops. Please, apply to learn more. More ❯

Quantum to share expertise, foster continuous learning, and rapidly re-use best practice ideas. Skills and experience we're looking for: Proficient in latest GenAI practiceslike prompt engineering and RAG systems, with awareness of emerging technologies such as Agentic AI. Skilled in integrating servicesvia Python-based pipelines, including package creation, and developing for user application interfaces. Knowledgeable in Kubernetes infrastructure More ❯

multi-step reasoning pipelines, and memory management using frameworks such as LangChain, CrewAI, and Autogen. Engineer and tune prompts to enhance the performance and reliability of generative tasks. Design RAG systems using vector databases like Pinecone, Chroma, and PosgreSQL for contextual retrieval. Incorporate semantic search and embedding strategies for more relevant and grounded LLM responses. Utilize Guardrails to implement applications … React for demoable outputs. Develop robust API integrations to connect AI agents with internal and external services and data sources. Use SQL to query structured databases and integrate with RAG or knowledge ingestion pipelines. Essential Skills Proficiency in Python programming. Experience in building multi-step intelligent systems. Knowledge of machine learning and artificial intelligence. Familiarity with frameworks like LangChain and More ❯

intelligence operations Design and architect multi-agent systems that can perform complex OSINT investigations autonomously Build agentic workflows using frameworks like LangChain, AutoGPT, CrewAI, n8n, or custom solutions Architect RAG systems that enable agents to access and reason over vast intelligence databases Design agent orchestration systems that coordinate multiple AI agents for complex investigative tasks Implement human-in-the-loop … and agent communication protocols Proficiency in advanced prompt engineering and chain-of-thought reasoning techniques Experience with function calling, tool use, and plugin architectures for LLMs Deep understanding of RAG architectures, vector databases, and semantic search Expert in Python and modern AI/ML frameworks Understanding of OSINT methodologies and intelligence analysis workflows Experience translating complex human workflows into automated More ❯

LangChain, CrewAI, and vector databases. Core Responsibilities Build and iterate on LLM/agent-based prototypes (e.g., copilots, chatbots, A2A agents). Implement multi-step reasoning, memory modules, and RAG pipelines. Use frameworks like LangChain, LangGraph, CrewAI, and tools like Pinecone, FAISS. Optimize performance and ensure responsible AI practices. Deploy via cloud platforms (AWS Bedrock, Azure AI, Google Vertex). More ❯

innovate in the fastest-moving fields of current AI research, in particular in how to integrate a broad range of structured and unstructured information into AI systems (e.g. with RAG techniques), and get to immediately apply your results in highly visible Amazon products. If you are deeply familiar with LLMs, natural language processing and machine learning, this may be the More ❯

LangChain, CrewAI, and vector databases. Core Responsibilities Build and iterate on LLM/agent-based prototypes (e.g., copilots, chatbots, A2A agents). Implement multi-step reasoning, memory modules, and RAG pipelines. Use frameworks like LangChain, LangGraph, CrewAI, and tools like Pinecone, FAISS. Optimize performance and ensure responsible AI practices. Deploy via cloud platforms (AWS Bedrock, Azure AI, Google Vertex). More ❯

LLM-Augmented Software Generation and Transformation – Senior Researcher Slough (UK) Our purpose is to make the world more sustainable. Fujitsu’s Research and Development (R&D) is at the forefront of Fujitsu's sustainability transformation strategy, conducting cutting-edge research to tackle social challenges and improve our future world. Your role will involve Conducting research and … development in automated software engineering, including but not limited to code representation for LLM, automatic program repair, generative AI for test generation, next-generation user interface for AI coding agents, and architecture for AI coding agents. Providing technical leadership and collaborating with Fujitsu Global R&D teams to develop novel software development technologies in the generative … tier conferences and journals. A passion for software engineering with the ability to write clean code. Proven expertise in machine learning, deep learning and AI, particularly in LLM and RAG technologies, with knowledge of LLM Agents, explainable AI and/or graph ML as a plus. Proficiency in Python, PyTorch and/or Tensorflow, and popular data science libraries, as More ❯

systems (retrieval, memory modeling, task orchestration) Integrate structured and unstructured knowledge from multiple modalities (text, image, video) into agent workflows Develop solutions coupling retrieval (KGs, RAG, databases) with planning, reasoning, and execution logic Collaborate with engineering teams on LLM platforms, search infrastructure, and agent systems Translate research into production-ready applications across AI development tools, QA … skills Experience working across research and applied development in a collaborative environment Keywords: Knowledge Graphs/LLMs/Semantic Search/Knowledge Reasoning/NLP/Agent Systems/RAG/OWL/SPARQL/Transformers/Deep Learning/AI Assistants/QA Systems/Pytorch/TensorFlow/Graph Reasoning/Multi-Modal AI/Knowledge Engineering/ More ❯

solutions that transform digital interactions. The role will focus on projects to leverage state-of-the-art generative AI, retrieval-augmented generation (RAG), and reasoning frameworks to build intelligent and context-aware systems. We are seeking talented Machine Learning Engineers with full-stack software development experience to join our client's team and … varied duties will include: Search relevancy engineering. Conversational AI Development : Design, train, fine-tune, and deploy LLMs with reasoning capabilities. Retrieval-Augmented Generation (RAG): Implement, optimise, and scale RAG pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models using techniques like LoRA, QLoRA … Milvus, ChromaDB, or OpenSearch. Required skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or Hugging Face Transformers Experience with RAG, LLM fine-tuning, and expertise in AWS and cloud-native AI deployments. Full-stack experience (React, TypeScript, Node.js) and API development. Familiarity with vector search and multi-agent orchestration Apply More ❯

solutions that transform digital interactions. The role will focus on projects to leverage state-of-the-art generative AI, retrieval-augmented generation (RAG), and reasoning frameworks to build intelligent and context-aware systems. We are seeking talented Machine Learning Engineers with full-stack software development experience to join our client's team and … varied duties will include: Search relevancy engineering. Conversational AI Development : Design, train, fine-tune, and deploy LLMs with reasoning capabilities. Retrieval-Augmented Generation (RAG): Implement, optimise, and scale RAG pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models using techniques like LoRA, QLoRA … Milvus, ChromaDB, or OpenSearch. Required skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or Hugging Face Transformers Experience with RAG, LLM fine-tuning, and expertise in AWS and cloud-native AI deployments. Full-stack experience (React, TypeScript, Node.js) and API development. Familiarity with vector search and multi-agent orchestration Apply More ❯

ll do: Contribute to the development of DDX — design, build, and scale intelligent systems Work on AI-driven features: LLMs, retrieval-augmented generation (RAG), and AI agents Build APIs, data pipelines, and backend components (mainly Python, FastAPI/Flask) Deploy microservice-friendly solutions, often in containerised setups (e.g. Docker) Work with ElasticSearch, Weaviate, Pinecone More ❯

and task orchestration. Drive the development of memory capabilities for intelligent agents, integrating structured and unstructured knowledge from multiple modalities. Architect solutions that deeply couple retrieval systems (RAG, KGs, databases) with agent planning, reasoning, and execution workflows. Work closely with LLM platforms, search infrastructure, and knowledge graph systems to build collaborative end-to-end agent solutions. Translate cutting More ❯

and accessibility tools. Comprehensive knowledge of Semantic Web technologies (RDF/s, OWL), query languages (SPARQL) and validation/reasoning standards (SHACL, SPIN). Comprehensive knowledge of RAG and GraphRag systems and architecture. Experience building ontologies in the e-commerce and semantic search spaces. Knowledge Graph and RAG -AI Architecture. Desirable: Experience with OCR, Image captioning, object detection More ❯

html Experience and Expertise Strong experience in GenAI, RAG and Agentic architectures, including popular frameworks like LangChain and LiteLLM. Expertise in Python, JavaScript, or Rust, coupled with experience deploying applications through frameworks such as FastAPI, Django, or React, within containerized environments using Docker and Kubernetes. Familiarity with DevOps and CI/CD pipelines, version control (Git). More ❯

Engineer The role is building AI based automation into back-office administration tasks. You'll be working with GenAI and Agentic AI, lots of AWS, NodeJS, Python, HuggingFace, LangChain, RAG techniques, interfacing with diverse data sets. The opportunity Work at the forefront of the industry. It's exciting, competitive, fast paced and challenging of course! You'll have the support More ❯

a First or high 2:1, preferably in a technical subject. Experienced in Python and its ecosystem. Diligently follow the space, and proactively try new AI technologies (Agents, MCP, RAG, Chain-of-Continuous-Thought, etc.). More ❯

high 2:1, preferably in a technical subject Proficiency in Python and its ecosystem Active engagement with the AI space and proactive exploration of new AI technologies (Agents, MCP, RAG, Chain-of-Continuous-Thought, etc. More ❯

time using image embeddings, similarity search (e.g. CLIP, vector search), and traditional CV approaches (e.g. YOLO, MobileNet). LLM & RAG Systems: Design and implement pipelines that support retrieval-augmented generation, internal AI tools, and scalable content delivery. Experience with vector databases, agent frameworks, or data workflows is highly relevant. Deployment & MLOps: Own model deployment More ❯

object detection (e.g. MobileNet, YOLO). Either way, we’re looking for someone who can help our app understand what the visitor is looking at – reliably and at scale. RAG Systems, Data Pipelines & Internal Agents: You'll design the data pipelines that power our AI features, including retrieval-augmented generation (RAG), internal LLM-based More ❯

object detection (e.g. MobileNet, YOLO). Either way, we’re looking for someone who can help our app understand what the visitor is looking at – reliably and at scale. RAG Systems, Data Pipelines & Internal Agents: You'll design the data pipelines that power our AI features, including retrieval-augmented generation (RAG), internal LLM-based More ❯

object detection (e.g. MobileNet, YOLO). Either way, we’re looking for someone who can help our app understand what the visitor is looking at – reliably and at scale. RAG Systems, Data Pipelines & Internal Agents: You'll design the data pipelines that power our AI features, including retrieval-augmented generation (RAG), internal LLM-based More ❯

object detection (e.g. MobileNet, YOLO). Either way, we’re looking for someone who can help our app understand what the visitor is looking at – reliably and at scale. RAG Systems, Data Pipelines & Internal Agents: You'll design the data pipelines that power our AI features, including retrieval-augmented generation (RAG), internal LLM-based More ❯

object detection (e.g. MobileNet, YOLO). Either way, we’re looking for someone who can help our app understand what the visitor is looking at – reliably and at scale. RAG Systems, Data Pipelines & Internal Agents: You'll design the data pipelines that power our AI features, including retrieval-augmented generation (RAG), internal LLM-based More ❯