8 of 8 Permanent Retrieval-Augmented Generation Jobs in West London

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 ...

emerging AI/ML capabilities within modern data platforms. Exposure to vector databases, embeddings, retrieval-augmented generation (RAG), and AI-ready data architectures. Retail, wholesale, supply chain, or large-scale transactional data experience. ...

Ready Data Architecture Architect data stores optimised for large language model (LLM) consumption, including vector databases, embedding pipelines, and retrieval‐augmented generation (RAG)‐compatible data structures that form the foundation of the AI product layer. Data Contracts & Platform Interfaces Partner with Business Analysts … cloud connectivity. AI & ML Enablement Practical experience designing data infrastructure to support AI/ML workloads, including vector stores, embedding pipelines, and integration with RAG‐based systems. Stakeholder & Communication Skills Excellent interpersonal skills, with the ability to explain complex data architecture concepts clearly to Business Analysts and non‐technical stakeholders. ...

platforms such as AWS, GCP, or Azure. What you’ll bring Deep, hands-on experience building and deploying production-grade AI systems using LLMs, RAG pipelines, MCPs, and agentic workflows Proven track record of leading complex, client-facing engagements with a forward-deployed mindset, partnering with both technical and business ...

tools, and cloud platforms such as AWS, GCP, or Azure. What you’ll bring: Proven hands-on experience building production-grade solutions using LLMs, RAG pipelines, MCPs, and agentic workflows A forward-deployed, client-first mindset, with experience working directly with both technical teams and business stakeholders Strong delivery capabilities ...

will be involved across the full lifecycle of GenAI productsfrom proof of concept through to scalable production deployment and monitoring. Working with modern LLMs, RAG architectures and agentic frameworks , youll collaborate with data engineers, solution architects and business stakeholders to deliver secure, cost-effective AI solutions on AWS. Key Responsibilities … Design and implement production GenAI applications using LLMs (e.g. Anthropic, AWS Bedrock models) Build and deploy RAG systems using vector databases and semantic search Develop agentic AI workflows using frameworks such as LangChain, LangGraph, CrewAI or similar Create effective prompt engineering strategies with appropriate guardrails for production systems Implement monitoring ...