12 of 12 Retrieval-Augmented Generation Jobs in Essex

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 ...

Requirements Demonstrable experience of deploying modern AI/ML solutions into production, including prompt engineering, retrieval-augmented generation (RAG), model evaluation, and monitoring using metrics (e.g. precision, recall, NDCG and drift detection). Strong Python skills with a grounding in software engineering best practices ...

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 ...

expertise in Java, React, and modern AI/ML development, including LLM frameworks, SDKs, and retrieval-augmented generation (RAG) systems. Key Responsibilities Design and develop scalable full-stack solutions involving both frontend (React/TypeScript) and backend (Java). Build and optimise AI/… pipelines leveraging LLMs and RAG architectures. Develop SDKs and APIs to support AI-driven applications in resource-constrained or production-grade environments. Collaborate across engineering, product, and research teams to drive innovation in AI/ML-based products. Ensure high performance, testability, and reliability across the stack. Key Qualifications Strong ...

Proven experience as an AI or Machine Learning Engineer with end-to-end model ownership Strong expertise in NLP and LLMs (transformers, fine-tuning, RAG, agents) Experience translating research and experimentation into production systems Solid understanding of MLOps, including CI/CD, monitoring and model lifecycle management Hands-on experience ...

Strong understanding of automation, workflow optimisation and process re engineering* Experience working within Agile teams* Understanding of AI technologies such as LLMs, NLPs and RAG pipelines* Excellent communication and stakeholder engagement skills* Experience facilitating workshops and discovery sessions What is on Offer * Salary up to £55,000* Hybrid working model ...

systems across internal tooling, developer workflows, and customer-facing products. You'll own the end-to-end build of production AI systems, from RAG pipelines and multi-agent architectures to LLM integration, fine-tuning, and workflow automation. Core Skills: Python/TypeScript/JavaScript LangChain RAG at scale LLM fine ...

moving environment Immediate impact: Your code will run in production and support real users from day one. Technical depth: Multi-agent reasoning, voice-streaming, RAG optimisation and all in one system. Flexible setup: Remote across the EU, with optional co-working in London or Barcelona. What you'll do Obsessive … productionise multi-agent LLM systems that reason, plan, and coordinate. Develop FastAPI-based microservices optimised for low latency and high reliability. Engineer and evaluate RAG pipelines: hybrid retrieval, re-ranking, grounding, and context validation. Integrate real-time voice interfaces (STT/TTS, WebRTC, LiveKit) into intelligent conversational flows. ...