1 to 25 of 79 Remote/Hybrid Retrieval-Augmented Generation Jobs in the UK

difference in people’s lives. Your Mission We’re seeking a highly experienced, technically exceptional Lead AI Engineer to architect and deliver next-generation Generative AI and Agentic systems. You will drive end-to-end innovation, from model selection and orchestration design to scalable backend implementation, all while … Claude, Gemini as well as self-hosted LLMs along with MCP integrations Implement Retrieval-Augmented Generation (RAG) techniques leveraging vector databases (Pinecone/ChromaDB/Weaviate/pgvector/etc), search engines such as ElasticSearch/Solr using both TF/IDF BM25 based ...

accelerate software development (using Github Copilot Coding Agents or similar) Desirable: Experience with prompt engineering, Retrieval-Augmented Generation (RAG), and graph databases Familiarity with multi-agent LLM systems and agentic platforms (e.g., AutoGen, CrewAI), and experience deploying LLM-based applications Experience with tools such ...

experience with: Production-level LLM applications including fine-tuning and customisation Function-calling systems or retrieval-augmented generation (RAG) architectures Full-lifecycle projects from prototyping to production Mentoring junior engineers or shaping technical direction within teams What You’ll Gain A high-impact role … with visibility and technical influence The opportunity to shape the next generation of AI platforms Hybrid working with three days per week onsite in Oxfordshire and two days remote A supportive team that values experimentation, clean engineering and continuous learning A salary ...

experience with: Production-level LLM applications including fine-tuning and customisation Function-calling systems or retrieval-augmented generation (RAG) architectures Full-lifecycle projects from prototyping to production Mentoring junior engineers or shaping technical direction within teams What You’ll Gain A high-impact role … with visibility and technical influence The opportunity to shape the next generation of AI platforms Hybrid working with three days per week onsite in Oxfordshire and two days remote A supportive team that values experimentation, clean engineering and continuous learning A salary ...

help drive innovation across cutting-edge AI initiatives — from chatbots and voice assistants to advanced retrieval-augmented generation (RAG) systems and agentic workflows. The Role You’ll work closely with the AI Engineering and Data Science teams to: Develop and prototype AI-driven solutions … across customer-facing and internal applications. Build and optimise LLM-based assistants , RAG pipelines , and agentic AI workflows . Collaborate on the architecture and deployment of scalable AI solutions (with support from engineering). Partner with stakeholders to translate business needs into practical, intelligent systems. Mentor junior team members ...

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 ...

. Multi-Agent Ecosystems: Design architectures that support Agent-to-Agent (A2A) communication, orchestration, and collaboration across diverse business functions. LLM Strategy & RAG: Define model selection, fine-tuning strategies, and build Retrieval-Augmented Generation (RAG) pipelines to ensure contextual accuracy and reduced hallucinations. Context ...

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 ...

application developers to translate business objectives into high-quality AI outcomes. A working understanding of Retrieval-Augmented Generation (RAG) is essential to ground model responses in authoritative enterprise content and to reduce hallucinations. This role blends hands-on engineering with rigorous experimentation, evaluation … . Utilize and manage RESTful APIs/SDKs to integrate model calls within web services, back-end jobs, and enterprise applications. Design and implement RAG pipelines (chunking, embeddings, indexing, ranking/citation policies) to ground responses in approved content stores. Establish offline/online evaluation frameworks (accuracy, safety, faithfulness, latency ...

intuitive user experiences. Work closely with AI researchers and ML engineers to integrate LLMs, Retrieval-Augmented Generation (RAG), and automation into production-ready applications. Ship robust, minimal-dependency code that performs efficiently in enterprise environments. Continuously iterate and refine AI-driven products, balancing user ...

intuitive user experiences. Work closely with AI researchers and ML engineers to integrate LLMs, Retrieval-Augmented Generation (RAG), and automation into production-ready applications. Ship robust, minimal-dependency code that performs efficiently in enterprise environments. Continuously iterate and refine AI-driven products, balancing user ...

intuitive user experiences. Work closely with AI researchers and ML engineers to integrate LLMs, Retrieval-Augmented Generation (RAG), and automation into production-ready applications. Ship robust, minimal-dependency code that performs efficiently in enterprise environments. Continuously iterate and refine AI-driven products, balancing user ...

intuitive user experiences. Work closely with AI researchers and ML engineers to integrate LLMs, Retrieval-Augmented Generation (RAG), and automation into production-ready applications. Ship robust, minimal-dependency code that performs efficiently in enterprise environments. Continuously iterate and refine AI-driven products, balancing user ...

intuitive user experiences. Work closely with AI researchers and ML engineers to integrate LLMs, Retrieval-Augmented Generation (RAG), and automation into production-ready applications. Ship robust, minimal-dependency code that performs efficiently in enterprise environments. Continuously iterate and refine AI-driven products, balancing user ...

party vendors. Knowledge of OO programming and software design, i.e., SOLID principles. Knowledge and working experience of AGILE methodologies. Familiarity with Databricks, and RAG application architecture a plus Experience with latency optimisation and quantisation preferred Additional Information What’s in it for you?: Competitive salary that reflects your skills, experience ...

Balancing cost, performance, and reliability Applied AI Working with Azure OpenAI APIs beyond prompt demos Retrieval-augmented generation (RAG) Embeddings, vector stores, and evaluation Guardrails, rate limiting, and failure handling Human-in-the-loop workflows Integration & automation Integrating AI into existing enterprise systems Automating ...

Engineer will be responsible for developing secure, scalable, and high-performing AI solutions, including document intelligence workflows, generative AI applications, and retrieval-augmented systems. This includes working on model orchestration, prompt engineering, document extraction pipelines, and system integrations with enterprise platforms such … intelligent document processing pipelines using Azure AI Doc Intelligence for tasks such as classification, extraction, OCR, and automated document workflows. Implement RAG (Retrieval-Augmented Generation) architectures using Azure Search or vector databases. Integrate AI capabilities with enterprise systems through REST APIs, Azure Functions, Logic ...

following skills and experience: Strong Python and SQL experience, ideally with Databricks Experience with ML and LLM tools (e.g., scikit learn, XGBoost, PyTorch, TensorFlow, RAG, APIs) Comfortable with data engineering basics and MLOps practices Experience working with cloud platforms such as Azure, AWS, or GCP Benefits The successful Senior ...

using Microsoft Copilot tools to automate business workflows (e.g., HR queries, IT support, operational data retrieval). Develop custom RAG (Retrieval-Augmented Generation) solutions within Databricks to allow LLMs to reason over proprietary SMS documents and data. Integrate AI agents with enterprise … Experience with defining metrics layers (e.g., DBX semantic layer or Databricks Genie) is essential. Practical experience with Large Language Models (LLMs), Prompt Engineering, and RAG architectures. Proficient in Python (for data manipulation and API interaction) and SQL (for data modelling). Experience working with REST APIs and connecting disparate business ...

microservices or serverless architectures Practical exposure to machine learning plus generative AI models including LLMs SLMs vision models Familiarity with AI frameworks such as RAG LangChain LlamaIndex plus agentic frameworks Hands-on cloud experience within Azure or AWS plus CI/CD tooling and Git Desirable Skills ...

Strong Python development experience within modern data science environments Proven track record building production LLM applications using commercial APIs Deep experience with prompt engineering, RAG architectures and function calling Production experience with cloud platforms including AWS, GCP or Azure Knowledge of containerisation and CI/CD practices Advanced AI Capability ...

Architect & Engineer AI Systems: You won’t just call an API; you will build robust processing architectures that leverage off-the-shelf LLMs, RAG pipelines, and context engineering to solve hard problems. You will benchmark these systems rigorously to ensure they actually work, not just "feel" smart. Build the Backend ...

health monitoring, and production readiness requirements. • GenAI Development Expertise: Demonstrated experience building production features using LLMs and GenAI technologies, including prompt engineering, model integration, RAG patterns, agentic workflows, and guardrails. Ability to define and measure quality, accuracy, and observability metrics for GenAI features in production. • Machine Learning & Data Science Knowledge ...

shape how GenAI and ML are used across the platform. Tech stack: Python PyTorch or TensorFlow scikit-learn Spark Kafka Databricks LangChain and RAG-based GenAI systems Cloud-native Azure environment Important Details: Fully remote across the UK Up to £80,000 - £100,000 (DOE) 2-3 stage interview process ...

experience and API development Experience with data pipelines and vector databases Knowledge of SRE, DevOps, and platform engineering practices Exposure to AI systems, LLMs, RAG, or agent architectures Experience working with UK government or secure environments You'll work alongside expert architects and engineers to deliver complex, high-impact solutions ...