1 to 25 of 42 Remote/Hybrid Retrieval-Augmented Generation Jobs in the UK excluding London

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 ...

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 ...

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 ...

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 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 ...

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 ...

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 ...

EventBridge, SQS, Step Functions Databases: DynamoDB, PostgreSQL, Aurora, MongoDB Frontend: React, TypeScript, MUI DevOps: AWS CDK, Terraform, Docker, GitLab CI, CodePipeline AI: Amazon Bedrock, RAG, LLM integrations Skills Required Commercial software engineering experience Strong AWS cloud/serverless experience Strong backend development skills in TypeScript, Python, or Go CI/ ...

/ML frameworks (e.g. LangChain, OpenAI API, Hugging Face, TensorFlow, PyTorch) * Comfortable writing production-ready code - Python particularly valued * Understanding of LLMs, prompt engineering, RAG pipelines or similar AI architectures * Ability to communicate technical ideas clearly to non-technical stakeholders * A proactive mindset - someone who spots opportunities and takes initiative ...

prioritise AI opportunities across the organisation Required ExperienceEssential Deep expertise in Python and relevant AI/ML frameworks and SDKs Proven experience building RAG pipelines that operate effectively in production environments Hands-on experience with model packaging, deployment, and production AI workflows Strong understanding of enterprise infrastructure concepts including VNets ...

Familiarity with AI-assisted development tools such as GitHub Copilot, Claude, and Cursor. Azure AI Engineer certification or equivalent practical experience. Understanding of MCP, RAG architectures, or vector databases. Benefits Industry-leading training and development opportunities. Competitive salary with progression potential. 23-days holiday allowance, increasing with length of service ...

agentic AI architectures, with experience integrating AI securely and at scale Practical experience in enterprise AI governance, prompt engineering, and deployment approaches (including RAG, fine-tuning considerations, and risk mitigation) Experience leveraging AI-assisted development tools (e.g. GitHub Copilot, Claude Code) to materially improve engineering productivity and delivery outcomes ...

prototypes, products and production ready solutions Designing and implementing end to end AI solutions that integrate with enterprise systems Working with LLMs, prompt engineering, RAG patterns, embeddings and fine tuning Developing AI agents and agentic workflows using modern frameworks Working with vector databases, APIs and modern data platforms Supporting … collaborative, high performing environment Experience Required: Strong Python and SQL experience Applied AI engineering, ML engineering or software engineering background Experience with LLMs, RAG, embeddings, prompt engineering or fine tuning Exposure to LangChain, LangGraph, Agent Development Kit or similar agent frameworks Experience with vector databases such as Pinecone, Chroma ...

deployment and monitoring. Lead end to end delivery of AI and LLM based solutions across underwriting and insurance journeys Build and deploy agentic workflows, RAG pipelines, and LLM powered applications for internal and customer facing products Work across structured and unstructured data, collaborating closely with Machine Learning and GenAI specialists ...

business environment Deep expertise in Python and modern AI/ML frameworks and SDKs Strong experience building and deploying production-grade AI solutions, including RAG pipelines and LLM-based applications Hands-on experience with AI deployment, operationalisation, monitoring, and scaling Strong understanding of enterprise infrastructure concepts including VNets, Entra ...

senior client stakeholders to shape and deliver solutions Build and deploy end-to-end AI products , not just prototypes Apply technologies such as LLMs, RAG systems, and agentic workflows to real business problems Translate technical outputs into clear commercial value and measurable impact Mentor and develop consultants, helping build capability ...

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 ...

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 ...

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 ...

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 ...