1 to 25 of 29 Retrieval-Augmented Generation Jobs in the North of England

features and applications using LLMs, including intelligent document processing, information extraction and automated workflows. Develop retrieval-augmented generation (RAG) solutions that connect LLMs with internal data, documents and other knowledge sources. Build and integrate APIs to connect applications, databases, CRM platforms and third-party … LLMs: Practical experience integrating large language models into applications or developing AI-powered functionality. Retrieval-Augmented Generation (RAG): Experience building or implementing RAG pipelines that enable LLMs to retrieve and use relevant information from documents, databases or other knowledge sources. Application architecture: An understanding ...

conversational assistants, workflow automation and intelligent document processing Developing applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and agent-based approaches Taking solutions from prototype through to deployment, monitoring and continuous improvement Building and maintaining APIs, integrations and data pipelines that support … Demonstrable ability to learn new technologies quickly Desirable Experience Agentic AI or AI agent frameworks Retrieval-Augmented Generation (RAG) Workflow automation platforms AI evaluation, monitoring or observability Experience working within regulated environments Agile delivery experience About YouThis organisation is placing particular emphasis on mindset ...

Agents & Automation Design, develop, and manage AI agents, copilots, and workflow automation solutions. Build Retrieval-Augmented Generation (RAG) applications leveraging internal business data. Develop intelligent assistants supporting operational, engineering, customer service, and business functions. Implement multi-agent workflows and orchestration frameworks. Monitor and optimise … enterprise environments. Strong Python development skills. Experience integrating and consuming REST APIs. Hands-on experience with Docker and Kubernetes. Knowledge of vector databases, embeddings, RAG architectures, and LLM orchestration frameworks. Experience with cloud technologies, networking, identity and access management, and security controls. Experience integrating AI solutions with Microsoft 365 technologies ...

Platform Engineer LLMs | RAG | Python | Cloud Infrastructure Manchester City Centre - 5 days a week onsite Up to £100,000 DOE The Opportunity We're looking for an AI Platform Engineer to build, optimise and maintain the infrastructure behind a growing portfolio of AI-powered applications. You'll focus on developing … scalable, reliable services supporting large language models (LLMs), retrieval-augmented generation (RAG), intelligent search and conversational AI. You'll take ownership of existing solutions while helping bring new AI capabilities into production. Key Responsibilities - Design, build and optimise production-grade AI platforms, RAG pipelines ...

Generative AI technologies. Microsoft Copilot Studio, Power Automate, Azure Logic Apps or Azure Functions. Retrieval-Augmented Generation (RAG) and Azure AI Search. Microsoft 365 or SharePoint integrations. Experience delivering AI solutions within enterprise environments. ...

industry is a plus. Strong foundational understanding of modern AI and agentic application patterns, including retrieval-augmented generation (RAG), semantic search, and agent-based system design. Hands-on experience building backend services, APIs, and production systems. Experience working across architecture, data, and platform concerns ...

Data, AI & Intelligent Systems Expertise: Experience or strong interest in one or more of the following: Generative AI, LLMs, Retrieval Augmented Generation (RAG) and AI applications Agentic AI, AI agents, intelligent assistants and AI-enabled automation Prompt engineering, AI evaluation and Responsible ...

GitHub Actions, Azure DevOps, RBAC and Cost Controls. Software Engineering: Clean Code (SOLID, DRY), DDD, modular architecture, TDD, code quality tools. · AI Engineering (Desirable): RAG pipelines, agentic workflows, MCP, LLM integration (OpenAI, Gemini or other models from Huggingface), LangSmith, TruLens. Agentic Frameworks (Desirable): LangChain, CrewAI, Autogen, OpenAI Agents, Google ADK. ...

Tableau or Power BI. Experience using GitHub Copilot or other AI-assisted software development tools. Exposure to agentic AI, workflow orchestration or retrieval-augmented generation. Key responsibilities Design and develop scalable data pipelines using Python, PySpark, Apache Kafka and Apache NiFi. Integrate vendor platforms and enterprise ...

ability to influence without line authority, including winning over sceptics Helpful: Bitbucket, Jenkins/Harness, Docker, Kubernetes, AWS, FastAPI, MCP, DORA-style measurement, RAG and vector search, self-hosted open-weight models, or a regulated environment such as gambling or financial services Guidant, Carbon60, Lorien & SRG - The Vertage Group Portfolio ...

frameworks, risk assessments, policies and controls. Practical understanding of how AI systems are built and secured. Good understanding of LLMs and agentic AI, including RAG, tool use and orchestration. Knowledge of AI security risks including prompt injection, data leakage and excessive agency. Strong understanding of relevant AI regulation and frameworks ...

enjoy building real solutions. You'll use Python as your primary development language, developing APIs, services and AI applications using technologies such as LLMs, RAG, AI agents and vector search. You'll work across the development lifecycle, helping take GenAI ideas and POC's through to robust, production-ready applications … enterprise clients. What you'll be doing: Build production applications and APIs using Python and FastAPI Develop LLM, RAG and AI agent solutions Build RAG pipelines covering embeddings, retrieval and generation Work with OpenAI, Anthropic, Mistral, Llama and similar LLMs Use LangChain, LangGraph, CrewAI or similar ...

across an organisation. Data, AI & Intelligent Systems Expertise: Strong understanding of modern AI capabilities, including Generative AI, Agentic AI, Retrieval Augmented Generation (RAG), intelligent assistants and AI-enabled automation, and enterprise AI ecosystems such as OpenAI, Google, Anthropic and Microsoft. Experience shaping enterprise ...

have experience building and operating scalable production applications with significant user numbers You have practical experience with LLMs and AI application development, including RAG, semantic search, LLMOps or agentic workflows You have an understanding of AI application reliability, evaluation and production monitoring You have experience with AWS and CI/ ...

across a range of AI and cloud projects. Responsibilities will include: Designing and delivering Generative AI and Machine Learning solutions Building applications using LLMs, RAG frameworks and AI orchestration tools Working directly with clients to understand business challenges and define AI-driven solutions Leading architecture and engineering decisions across Azure … experience building AI-powered applications and deploying them into production Software Engineering or Product Engineering background Strong Python development skills Experience working with LLMs, RAG architectures and modern AI frameworks Multi-cloud experience across Azure, AWS and GCP Strong understanding of cloud-native architectures and scalable application design Experience with ...

enjoy building real solutions. You'll use Python as your primary development language, developing APIs, services and AI applications using technologies such as LLMs, RAG, AI agents and vector search. You'll work across the development lifecycle, helping take GenAI ideas and POC's through to robust, production-ready applications … enterprise clients. What you'll be doing: Build production applications and APIs using Python and FastAPI Develop LLM, RAG and AI agent solutions Build RAG pipelines covering embeddings, retrieval and generation Work with OpenAI, Anthropic, Mistral, Llama and similar LLMs Use LangChain, LangGraph, CrewAI or similar ...

fundamentally change how analysts work and how Fitch operates Drive breakthrough AI innovation and experimentation boldly – Lead exploration of generative AI, multi-agent systems, RAG architectures, model fine-tuning, prompt engineering, and other emerging ML technologies; create cutting-edge proofs-of-concept; evaluate what's transformative versus what's hype … environments Advanced generative AI expertise – Extensive hands-on experience developing and integrating generative AI solutions, working with large language models, building agentic systems, implementing RAG architectures, and training/fine-tuning neural networks using frameworks like PyTorch Bachelor's degree in Machine Learning, Computer Science, Data Science, Applied Mathematics ...

Model programme for a central government department. Where the Lead Technical Architect sets direction, you turn it into working, production-grade systems - semantic search, RAG pipelines, and broader generative AI capability - integrated into complex Legacy and multi-cloud environments handling high-volume, sensitive public sector data. You'll work inside … eventually run these systems without long-term reliance on external suppliers. Key ResponsibilitiesHands-On Build & Delivery Design, build, and ship production AI components - RAG pipelines, retrieval and embedding infrastructure, orchestration logic, and integration layers - writing high-quality, tested, maintainable code and staying close to implementation rather than delegating ...

Model programme for a central government department. Where the Lead Technical Architect sets direction, you turn it into working, production-grade systems - semantic search, RAG pipelines, and broader generative AI capability - integrated into complex Legacy and multi-cloud environments handling high-volume, sensitive public sector data. You'll work inside … eventually run these systems without long-term reliance on external suppliers. Key Responsibilities Hands-On Build & Delivery Design, build, and ship production AI components - RAG pipelines, retrieval and embedding infrastructure, orchestration logic, and integration layers - writing high-quality, tested, maintainable code and staying close to implementation rather than ...

client is a global professional services and technology consultancy. We are building the next generation of AI-native engineering talent engineers who use AI as a core part of how they work, not as an add-on. As an AI Engineer (Software), you will design, build, and ship … improve AI-generated outputs; understanding of AI limitations and responsible use Familiarity with agentic system concepts awareness of orchestration frameworks (LangChain, LangGraph, or equivalent), RAG pipelines, and how full-stack applications connect to agent-based architecture; production experience preferred, conceptual understanding required About our client Our client is a leading ...

direct pathway to the Forward Deployed Engineer programme. Key Responsibilities Design and build production-grade agentic solutions end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning … depth, not tutorial level Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering LLMOps fundamentals: eval harness design, prompt versioning, and production observability Cloud-native engineering maturity: Kubernetes ...

interpret uncertainty, and support sound decisions. Build and evaluate generative AI capabilities within Nexus (AWS stack), including prompt and context design, embeddings, retrieval-augmented generation, knowledge bases, and agent or tool behaviours. Write production-quality Python and SQL, and work with engineers to integrate ...

across products, services and internal operations to improve customer value, automation and decision-making. Explore and apply emerging technologies including generative AI, LLMs, RAG, Agentic AI, predictive analytics, graph intelligence, recommendation systems and semantic search. Turn ideas into prototypes and MVPs, validate them with users and stakeholders, and scale successful … solutions, not just experimentation or proof of concepts. Strong hands-on experience with generative AI, LLMs and AI application architectures, including RAG, prompt engineering, agents and model evaluation. Deep understanding of modern data engineering, data platforms and MLOps. Strong cloud experience across Azure with experience building scalable, cloud-native solutions. ...

across Artificial Intelligence and Machine Learning. We are open to candidates with different areas of expertise, including Machine Learning, Applied AI, Generative AI, LLMs, RAG and Agentic AI. Key Responsibilities Design and develop AI/ML solutions from early-stage concepts through to production. Develop, evaluate and integrate AI/… models and applications. Work across modern AI technologies including LLMs, RAG, NLP, Computer Vision and agent-based systems. Assess model performance, investigate failure modes and optimise solutions. Develop robust, high-quality Python software for technically complex applications. Work closely with experienced engineers, technical specialists and customers to solve challenging real ...

real AI products, tackling challenges around retrieval, model performance, accuracy and evaluation. What you'll be working on: Building and improving RAG pipelines, including retrieval strategies, chunking, reranking and grounding. Developing GenAI applications and agentic workflows using tools such as LangChain, LangGraph or similar frameworks. Working … platform teams. What we're looking for: Strong experience with model evaluation, experimentation and fine-tuning. Solid hands-on experience building and deploying RAG and GenAI solutions. Good understanding of LLMs, embeddings, vector databases and retrieval techniques. Experience with Azure AI Foundry would be advantageous. Someone who enjoys ...