9 of 9 Permanent Retrieval-Augmented Generation Jobs in Lancashire

Engineer GenAI | RAG | AI Agents | Python | SQL Locations: Manchester, Leeds, Edinburgh (preferred) | Birmingham & Bristol considered - hybrid working - 2 days per week in either of the above locations Contract initially until Dec 2026. Day rate: up to £800 inside IR35 via umbrella The Opportunity We're looking for an experienced … business teams to deliver innovative AI solutions. Essential Skills & Experience Commercial experience building Generative AI solutions. Strong hands-on experience with RAG (Retrieval-Augmented Generation). Experience developing AI Agents or Agentic AI applications. Strong Python development skills. Strong SQL skills. Experience integrating applications ...

existing business systems and platforms using REST APIs and cloud-native architectures. Build and optimise Retrieval-Augmented Generation (RAG) solutions, embeddings, and vector database implementations. Develop AI Agents and Multi-Agent Systems to automate complex business processes. Apply prompt engineering and context engineering techniques … business stakeholders to drive AI adoption and innovation. Required Experience: Strong background in Generative AI, LLMs, NLP, and AI Engineering. Hands-on experience with RAG, embeddings, and vector databases. Proven experience developing AI Agents and agentic workflows. Proficient in Python, SQL, and API integration. Familiarity with cloud platforms such ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

build and deploy enterprise-grade AI solutions across an Enterprise Knowledge Base (EKB). This is a hands-on role focused on GenAI, LLMs, RAG and Agentic AI, taking solutions from prototype through to production. You'll work across AI, data and engineering teams to build intelligent retrieval, knowledge discovery and automation capabilities. Key Responsibilities Build production-grade GenAI, LLM, RAG and Agentic AI solutions Develop AI Agents and Multi-Agent Systems Work with LangChain, LangGraph and MCP Build intelligent retrieval using Embeddings, Vector Databases and Semantic Search Apply Prompt & Context Engineering Integrate ...

Neo4j/Knowledge Graph experience Experience with Vector Databases, Search or Indexing Strong understanding of data modelling and governance Experience supporting AI, LLM or RAG use cases Nice to have: Enterprise Knowledge Management/Knowledge Base experience Semantic Search Large-scale Search platforms AI/LLM and RAG experience Financial ...

attention to detail. Ability to work effectively within Agile squads and cross-functional environments. Desirable: Exposure to AI testing methods (e.g., validating LLM outputs, RAG consistency, AI behaviour evaluation). Experience using AI-powered QA tools or test-generation frameworks. Knowledge of cloud environments (AWS/Azure ...

client teams to design, build and deploy AI driven solutions on the platform Translate complex research and business questions into production ready LLM and RAG workflows Debug, optimise and scale LLM applications in real world, client facing environments Build and integrate data pipelines and services across cloud platforms and modern … Your Skills and Experience Strong commercial engineering experience in AI, machine learning or data focused roles Hands on experience with retrieval augmented generation and production LLM workflows Confidence debugging and improving end to end LLM systems in live environments Experience working with modern data ...