12 of 12 Retrieval-Augmented Generation Jobs in Manchester

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Identify and deliver AI opportunities across client services, professional delivery, internal operations, knowledge management and commercial growth. Work with Generative AI, LLMs, Machine Learning, RAG, vector search and knowledge graphs. Build and lead a cross-functional AI team, bringing together engineers and professional subject matter experts. Establish practical AI governance … opportunities into measurable client and commercial outcomes. About you Strong hands-on experience taking Machine Learning and Generative AI solutions into production, including RAG architectures. Deep technical knowledge of LLMs, NLP, Python, SQL and frameworks such as PyTorch or TensorFlow. A solid understanding of MLOps, DevOps, CI/ ...