1 to 25 of 279 Retrieval-Augmented Generation Jobs in England

Data/RAG Engineer - watsonx.ai & Data Contract | July-December 2026 Up to £550 per day | Inside IR35 UK-based | Mostly Remote (occasional onsite required) British-born nationals or UK residents (5+ years) | One passport only Previous SC clearance highly advantageous Build AI-driven solutions using watsonx We're looking … experienced Data/RAG Engineer to support the delivery of a high-profile AI MVP using watsonx.ai and watsonx.data . This role will play a key part in designing, implementing and optimising Retrieval-Augmented Generation (RAG) pipelines and data foundations that enable LLM-driven ...

/state handling, and fallback logic Implement and consume vector store integrations , prompt templates, and retrieval-augmented generation (RAG) techniques Ensure workflows are robust, secure, and observable , with proper logging, monitoring, and exception handling Partner with Product Managers, SMEs, and QA to translate business … monitoring tools to track agent performance. Exposure to knowledge graphs, memory management systems, or retrieval-augmented generation (RAG) pipelines. ...

/state handling, and fallback logic Implement and consume vector store integrations , prompt templates, and retrieval-augmented generation (RAG) techniques Ensure workflows are robust, secure, and observable , with proper logging, monitoring, and exception handling Partner with Product Managers, SMEs, and QA to translate business … monitoring tools to track agent performance. Exposure to knowledge graphs, memory management systems, or retrieval-augmented generation (RAG) pipelines. ...

developing scalable Python-based applications within enterprise environments Building and optimising AI-driven solutions, including Retrieval-Augmented Generation (RAG) capabilities Working with graph databases and integrating complex data relationships into AI workflows Containerising applications and deploying services into Kubernetes environments Improving system performance, reliability … within large-scale organisations Hands-on AI engineering expertise, ideally within production environments Experience with Retrieval-Augmented Generation (RAG) and Microsoft Graph Retrieval Strong understanding of Kubernetes, containerisation, and deployment practices Experience building and maintaining CI/CD pipelines Knowledge of graph ...

Engineer – RAG & Knowledge Systems Onsite (London, UK) | Hybrid Option Available Experience: 3+ years AI | Generative AI | Knowledge Systems About the Role We’re looking for an LLM Engineer to design and implement retrieval-augmented generation (RAG) systems powering next-generation AI applications. … knowledge and document systems Retrieval pipelines combining: embeddings vector databases LLM inference Production-grade generative AI workflows Key Responsibilities Design and implement RAG architectures and retrieval pipelines Develop systems using LLMs, embeddings, and vector databases Optimise relevance, latency, and response quality of AI systems Integrate ...

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. ...

Data & AI, Index Engineering, and Index Product teams to explore how large language models (LLMs), retrieval-augmented generation (RAG), and agentic systems can transform how MSCI designs, evaluates, and communicates index methodologies. The team operates at the intersection of AI research, applied engineering … domain expertise to shape the next generation of MSCI Index capabilities. Your Key Responsibilities: Design and develop advanced AI systems leveraging LLMs, RAG, and agentic architectures to enhance index research, methodology interpretation, and automation. Prototype and validate new AI concepts, translating research ideas into working, production-ready solutions. ...

design and deploy intelligent AI systems powered by: Large Language Models (LLMs) Agentic AI frameworks Retrieval-Augmented Generation (RAG) Multi-agent orchestration Tool-using autonomous workflows This is a hands-on engineering role focused on production delivery, scalability, reliability, and business impact. Your Work … Will Include Building AI agents with reasoning, planning, memory, and tool orchestration Developing advanced RAG pipelines and context-aware AI systems Designing MCP-style architectures and interoperable AI workflows Creating recommendation, forecasting, and classification models on large-scale datasets Automating complex business operations using AI-driven decision systems Integrating ...

that automatically generate structured player summary reports from statistical profiles or unstructured scouting notes Build retrieval-augmented generation (RAG) pipelines over internal scouting databases, enabling natural-language queries against recruitment records Automate repetitive analytical tasks so the recruitment team receives live, structured outputs ...

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 ...

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 ...