1 to 25 of 77 Remote/Hybrid Retrieval-Augmented Generation Jobs in England

team is building next-generation AI solutions using Large Language Models, Agentic AI, Retrieval-Augmented Generation (RAG), and intelligent automation to drive measurable business outcomes. Skills & Responsibilities Build and deploy production-grade AI agents and LLM-powered applications. Design agentic workflows using … LangChain, LangGraph, MCP, FastMCP, or similar frameworks. Develop and optimise Retrieval-Augmented Generation (RAG) systems. Integrate AI solutions into products, APIs, and business workflows. Work with AWS, Azure, or GCP to deploy scalable AI systems. Contribute to recommendation, classification, forecasting, and automation initiatives. Strong ...

Machine Learning solutions into production environments Build and optimise Generative AI applications using Large Language Models (LLMs) Develop Retrieval Augmented Generation (RAG) architectures and AI agents Build speech-to-text, voice AI and accessibility-focused solutions Create intelligent chatbots, virtual assistants and conversational … Learning Commercial experience developing Machine Learning solutions Experience working with Large Language Models (LLMs) Knowledge of Generative AI technologies RAG (Retrieval Augmented Generation) implementation experience NLP and Conversational AI development Model training, testing and evaluation Prompt Engineering and AI workflow optimisation Programming & Frameworks Strong ...

tool execution. Integrate AI agents with APIs, databases, enterprise systems, and third-party platforms. Develop Retrieval-Augmented Generation (RAG) solutions using vector databases and knowledge repositories. Create secure, scalable, and production-ready AI workflows. Engineer prompts, system instructions, and evaluation frameworks to improve agent … memory, reflection, and tool use. Experience building autonomous AI agents and AI-powered automation solutions. Strong prompt engineering and AI evaluation experience. Experience implementing RAG architectures and semantic search solutions. Software Engineering Strong Python development skills. Experience with JavaScript and/or TypeScript. Experience building and integrating REST APIs. Understanding ...

hands-on with Python and Azure , possessing a proven track record of designing and deploying Retrieval-Augmented Generation (RAG) patterns. Key Responsibilities Architect & Govern: Design and implement robust on-premises, cloud, and hybrid AI solutions, ensuring strict technical governance and best practices across … client relationships to secure consensus on complex technical architectures. Essential Skills Clearance: Active UK Government Security Check (SC). GenAI: Expertise in LLMs and RAG architectures. Python: Strong proficiency alongside PySpark for data engineering. Azure: Deep familiarity with native tools (AI Search, Document Intelligence). MLOps: Understanding of LLMOps frameworks ...

hands-on with Python and Azure , possessing a proven track record of designing and deploying Retrieval-Augmented Generation (RAG) patterns. Key Responsibilities Architect & Govern: Design and implement robust on-premises, cloud, and hybrid AI solutions, ensuring strict technical governance and best practices across … client relationships to secure consensus on complex technical architectures. Essential Skills Clearance: Active UK Government Security Check (SC). GenAI: Expertise in LLMs and RAG architectures. Python: Strong proficiency alongside PySpark for data engineering. Azure: Deep familiarity with native tools (AI Search, Document Intelligence). MLOps: Understanding of LLMOps frameworks ...

deploy Generative AI systems end to end – from use-case discovery through to production Build Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, and enterprise data sources Develop agent-based workflows, orchestration layers, and multi-step reasoning systems Work extensively with open-source … experience building and deploying Generative AI or applied ML systems Strong hands-on engineering background – you actively code, build, test, and deploy Experience with: RAG architectures and vector databases Hugging Face models, pipelines, and inference tooling Prompt engineering and LLM evaluation techniques Agentic systems and orchestration patterns Cloud-native experience ...

generative AI solutions for scientific and knowledge-discovery applications. Develop LLM-powered workflows and retrieval-augmented generation (RAG) systems for search, summarization, question answering, and evidence-grounded insight generation. Build intelligent retrieval, ranking, recommendation, and decision-support capabilities using modern orchestration frameworks … skills and solid machine learning fundamentals. Experience working with large-scale text or content-rich datasets and modern AI/ML frameworks. Experience with RAG, semantic, vector, or hybrid search, along with experimentation and evaluation approaches that measure user impact. Familiarity with cloud platforms and modern software engineering practices. Strong ...

tooling Copilot Studio Power Platform Enterprise Data Platforms Semantic Kernel LangChain Microsoft Agent Framework Retrieval-Augmented Generation (RAG) LLM-powered applications and intelligent agents MLOps, LLMOps and Cloud Native Architectures What we're Looking For You're likely already operating as an AI Architect … across both Generative AI and traditional Machine Learning Experience taking AI products from concept to production and ongoing operation Deep understanding of LLM architectures, RAG patterns and agent-based solutions Strong Azure and Microsoft ecosystem knowledge Experience designing secure, scalable and governed AI platforms Consulting experience with the ability ...

intelligent, distributed systems Architecting and building AI-driven capabilities , including: Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) AI agents and orchestration workflows Designing and maintaining backend services and APIs (primarily Python, FastAPI/Flask) Building and scaling data pipelines and search systems using … Python or similar languages Significant experience with backend development , microservices, and distributed systems Hands-on experience with AI/ML systems , including LLMs, RAG, or vector databases Solid understanding of containerisation and orchestration (Docker/Kubernetes) Experience delivering secure, on-premise or regulated systems Proven ability to lead technically , mentor ...

Responsibilities Design & Deliver: Architect and implement cloud and AI-ready solutions on Azure, building repeatable patterns and integration services that support next-generation workloads. Secure AI Architecture: Develop and scale secure infrastructure architectures that allow applications to safely access both cloud-based backend services and on-premises data … Beyond the Basics): Real-world enterprise experience designing and deploying solutions centred around agentic workflows, Retrieval-Augmented Generation (RAG), and vectorisation patterns, rather than purely self-taught or personal interest projects. Software Engineering & Tech Stack: High proficiency in Python and PowerShell for building automation ...

architectures on AWS, accelerating delivery of conversational AI products using Amazon Bedrock, agentic frameworks, and retrieval-augmented generation (RAG) patterns. Key responsibilities: Own the target-state architecture for GenAI products (voice and chat), including multi-account strategy, landing zone alignment, network patterns, and environment … segregation (dev/test/prod). Define reference architectures for Amazon Bedrock, model orchestration, RAG, tool/function calling, and agentic workflows (e.g., LangChain/LangGraph/CrewAI/Strands-style patterns) with clear guardrails. Design enterprise-grade knowledge architectures: ingestion pipelines, chunking strategies, embedding/model choices, vector ...

Language Models (LLMs) and understanding of trade-offs between performance, cost, and capability. Understanding of Retrieval-Augmented Generation (RAG), agent orchestration, prompt engineering, and tool calling. Familiarity with AI standards such as Model Context Protocol (MCP) and Agent2Agent (A2A). Familiar with AI orchestration ...

experience with cloud platforms (AWS, Azure, GCP) Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and Gen AI/Agentic frameworks (LangGraph, LangChain, VectorDBs, RAG) Understanding of responsible AI practices, including guardrails, hallucination mitigation, and output quality management for AI systems Experience designing and scaling low-latency, customer-facing ...

experience with cloud platforms (AWS, Azure, GCP)Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and Gen AI/Agentic frameworks (LangGraph, LangChain, VectorDBs, RAG)Understanding of responsible AI practices, including guardrails, hallucination mitigation, and output quality management for AI systems Experience designing and scaling low-latency, customer-facing ...

experience using frameworks such as PyTorch, TensorFlow, LangChain, or similar. * Experience working with LLMs, prompt engineering, vector databases, and retrieval augmented generation (RAG) architectures. * Strong knowledge of cloud platforms including AWS or Azure. * Experience with data engineering and orchestration tools such as Databricks, Airflow ...

implementing end-to-end AI/ML solutions from development through to deployment and monitoring * Building scalable Generative AI and Agentic AI solutions, including RAG pipelines and orchestration frameworks * Implementing MLOps frameworks, governance and best practices * Translating business requirements into cloud-native AI and data architectures across Azure ...

clearance (requires 5 years of continuous UK residency) Desirable Experience Experience with Agentic frameworks such as LangChain, CrewAI, Autogen, or OpenAI Agents Familiarity with RAG pipelines, MCP, and LLM evaluation tools like LangSmith or TruLens Knowledge of software engineering best practices, including Clean Code (SOLID), TDD, and modular architecture Experience ...

tooling) AWS/Azure cloud platforms (including AI services such as Bedrock) Data engineering tools (e.g. AWS Glue or equivalent) NLP, embeddings, vector databases, RAG architectures MLOps tooling for model deployment and monitoring Experience with distributed data systems and APIs Required Skills & Experience Several years' experience in software engineering, data ...

focused on developing next-generation agentic AI systems. This is an opportunity to work hands-on with modern AI technologies including LLMs, RAG pipelines, vector databases, and multi-agent workflows while delivering production-grade AI solutions across multiple business domains. What You’ll Do Design and build agentic … well-tested Python code What We’re Looking For 4+ years of experience in Python engineering Hands-on experience with: RAG (Retrieval-Augmented Generation) Agentic workflows Vector databases Experience with LangChain and LangGraph Experience with cloud platforms such as Azure, AWS, or GCP Good ...

Regulatory compliance automation Document intelligence (KYC, contracts, trade docs) Customer insights & conversational AI You will work on cutting-edge technologies including LLMs, vector databases, RAG architectures, and cloud-native data platforms, helping organizations unlock value from structured and unstructured data. Hybrid working: The places that you work from … time. Your Role: Design and build end-to-end Gen AI pipelines using LLMs (Open AI, Azure Open AI, Anthropic) Develop RAG architectures with vector databases (Pinecone, FAISS, Azure AI Search) Optimize LLM outputs using prompt engineering, embeddings, and evaluation techniques Build AI solutions for document processing, compliance, and banking ...

technologies Experience architecting scalable AI and ML solutions across serverless, containerised or GPU enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience ...

technologies Experience architecting scalable AI and ML solutions across serverless, containerised or GPU enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience ...

datastores AI & Automation Hands-on experience integrating LLMs, AI APIs (OpenAI, Azure OpenAI, etc.), or ML models into production applications Familiarity with agentic frameworks, RAG pipelines, or AI-assisted workflow automation Ability to assess where AI genuinely adds value and communicate that clearly to non-technical stakeholders Mindset & Adaptability Proven ...

Required Experience Strong programming skills, particularly in Python Experience with machine learning and AI, including LLMs or related technologies Knowledge of areas such as RAG architectures, agent-based AI, computer vision, or audio processing Experience working with cloud platforms (AWS or Azure preferred) Familiarity with Docker, Linux/Unix environments ...

delivering a modern enterprise data platform. This opportunity offers the chance to work on large-scale data initiatives, modern cloud technologies and next-generation analytics capabilities that will support strategic decision-making across the business. The Opportunity You will join a growing data function responsible for building … environments Desirable Experience Microsoft Fabric Power BI development PySpark REST API integrations Lakehouse architectures AI and Machine Learning platforms Retrieval Augmented Generation (RAG) Vector databases Generative AI solutions Power Platform technologies Data transformation or modernisation programmes What We're Looking For We're looking ...