1 to 25 of 298 Retrieval-Augmented Generation Jobs in the UK

employees located in the US, Dubai, Mexico, India, China, and Singapore. Information about the Learning Facilitator role: Emeritus is seeking a Program Facilitator in RAG and Context Engineering: Designing and Building Production-Grade AI Systems for MIT xPRO. The Program Facilitator should function as both: a technical subject matter expert … artificial intelligence, machine learning or any related fields. Hands-on experience building or experimenting with Retrieval-Augmented Generation (RAG) systems. Experience working with LLM APIs (e.g., OpenAI, Anthropic, OpenRouter, Gemini). Experience implementing or testing AI-powered applications. Familiarity with vector databases, embeddings, semantic ...

Senior AI Engineer (Gen AI & RAG) Greater London, England, United Kingdom (On-site) Join an award-winning, internationally recognised B2B Consultancy as a Senior AI Engineer, where you will play a central role in delivering and evolving production-grade generative AI capabilities that power real-world business outcomes. This … intelligence layer of next-generation AI products. You will architect and implement Retrieval-Augmented Generation (RAG) pipelines, agentic workflows, and rigorous evaluation frameworks that ensure AI systems are accurate, reliable, and grounded in genuine business value. Reporting to the Director ...

team is building next-generation AI solutions using Large Language Models, Agentic AI, Retrieval-Augmented Generation (RAG), and intelligent automation to drive measurable business outcomes. Skills & Responsibilities Build and deploy production-grade AI agents and LLM-powered applications. Design agentic workflows using … LangChain, LangGraph, MCP, FastMCP, or similar frameworks. Develop and optimise Retrieval-Augmented Generation (RAG) systems. Integrate AI solutions into products, APIs, and business workflows. Work with AWS, Azure, or GCP to deploy scalable AI systems. Contribute to recommendation, classification, forecasting, and automation initiatives. Strong ...

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 ...

delivering AI/ML or generative AI systems in production Strong understanding of LLMs , including: Retrieval-Augmented Generation (RAG) Experience building or delivering agentic AI systems Strong Python engineering skills and experience building API-based services Experience with modern data architectures and system design ...

generative AI systems in production Deep understanding of LLMs , including: Prompt engineering Embeddings Fine-tuning Retrieval-Augmented Generation (RAG) Demonstrated experience building and scaling agentic AI systems Strong experience with AI system design, architecture, and distributed systems Expertise in API-based backend development (e.g. ...

including AWS, Azure, or Google Cloud. Experience with generative AI, large language models (LLMs), and retrieval-augmented generation (RAG). Technical Skills Python SQL Machine Learning MLOps Cloud Computing Data Engineering Statistical Analysis Core Competencies Cross-functional collaboration Critical thinking Communication skills Project management … meaningful contribution to healthcare innovation. If you are ready to develop your skills, collaborate with talented professionals, and help create the next generation of AI driven healthcare technologies, we would love to hear from you. ...

powered features or workflows Experience with OpenAI, Anthropic, Azure AI, LangChain, LlamaIndex, or similar technologies Retrieval-Augmented Generation (RAG) solutions AI agents and workflow automation What's on Offer £300 per day Fully remote working Outside IR35 engagement Rolling 3-month contract with extension ...

teams to design, develop, and scale production-ready solutions using the latest AI technologies and cloud-native engineering practices. Key Skills: AI & GenAI: LLMs, RAG, Agents, LangChain, LangGraph, CrewAI, Vercel AI SDK, OpenAI, Anthropic, Mistral, Llama. Full Stack Engineering: Python, TypeScript, React, APIs, Microservices, FastAPI. Cloud & DevOps: AWS/Azure … with the team to develop GenAI proof-of-concepts (POCs) for clients using technologies like Retrieval-Augmented Generation (RAG) and intelligent agents. Scale existing POCs to production-ready solutions for customer use. Design and develop Full Stack applications for both GenAI and non-GenAI ...

teams to design, develop, and scale production-ready solutions using the latest AI technologies and cloud-native engineering practices. Key Skills: AI & GenAI: LLMs, RAG, Agents, LangChain, LangGraph, CrewAI, Vercel AI SDK, OpenAI, Anthropic, Mistral, Llama. Full Stack Engineering: Python, TypeScript, React, APIs, Microservices, FastAPI. Cloud & DevOps: AWS/Azure … with the team to develop GenAI proof-of-concepts (POCs) for clients using technologies like Retrieval-Augmented Generation (RAG) and intelligent agents. Scale existing POCs to production-ready solutions for customer use. Design and develop Full Stack applications for both GenAI and non-GenAI ...

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 ...

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 ...

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 ...

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 ...

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 ...