12 of 12 Retrieval-Augmented Generation Jobs in the East of England

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 ...

architectures on AWS, accelerating delivery of conversational AI products using Amazon Bedrock, agentic frameworks, and retrieval-augmented generation (RAG) patterns. Key responsibilities: Own the target-state architecture for GenAI products (voice and chat), including multi-account strategy, landing zone alignment, network patterns, and environment … segregation (dev/test/prod). Define reference architectures for Amazon Bedrock, model orchestration, RAG, tool/function calling, and agentic workflows (e.g., LangChain/LangGraph/CrewAI/Strands-style patterns) with clear guardrails. Design enterprise-grade knowledge architectures: ingestion pipelines, chunking strategies, embedding/model choices, vector ...

Language Models (LLMs) and understanding of trade-offs between performance, cost, and capability. Understanding of Retrieval-Augmented Generation (RAG), agent orchestration, prompt engineering, and tool calling. Familiarity with AI standards such as Model Context Protocol (MCP) and Agent2Agent (A2A). Familiar with AI orchestration ...

models (DAX and Power Query) Exposure to ML/AI concepts (e.g. scikit-learn, TensorFlow) or an interest in preparing data for LLMs, RAG architectures or predictive models Experience with Docker and/or Terraform (desirable) What You Will Learn & What Liquidline Can Offer You Being a part of Liquidline ...

SaaS, fintech, professional services or regulated environments Exposure to AI-enabled products, automation tools or GenAI initiatives Understanding of technologies such as LLMs, NLP, RAG pipelines or AI workflow tooling Strong stakeholder management and workshop facilitation skills Experience working in Agile product environments Commercially minded product thinking with a focus ...

SaaS, fintech, professional services or regulated environments Exposure to AI-enabled products, automation tools or GenAI initiatives Understanding of technologies such as LLMs, NLP, RAG pipelines or AI workflow tooling Strong stakeholder management and workshop facilitation skills Experience working in Agile product environments Commercially minded product thinking with a focus ...

rather than purely research-focused individuals. You will work on a broad range of AI challenges spanning multimodal AI, machine learning, computer vision, LLMs, RAG architectures, transformer models, audio processing and intelligent automation features within a highly collaborative engineering environment. Location: Cambridge, UK. Hybrid working model - minimum of 4 days … Kotlin, TypeScript and React, prior experience with these technologies is not required Interest and exposure across AI technologies such as machine learning, LLMs, RAG systems, computer vision, multimodal AI or transformer architectures Ability to work across both research/prototyping and production engineering Strong problem-solving ability and analytical thinking ...

value (and where it doesn’t) Use, advise on, and develop AI‐related features such as: Intelligent search/retrieval experiences and RAG Summarisation and document comprehension at scale Context management AI agents, MCP servers, and best‐practice configuration of, for example, AGENTS.md Advanced use of tools such … refactoring support, documentation acceleration, internal workflows) Building products that incorporate AI/LLM features (e.g. assistants/coplots, summarisation, document understanding, semantic search/RAG, extraction, classification, decision support, workflow automation) Strong engineering fundamentals You build with the discipline needed to keep software reliable and maintainable, including: automated testing strategies ...

SaaS, fintech, professional services or regulated environments Exposure to AI-enabled products, automation tools or GenAI initiatives Understanding of technologies such as LLMs, NLP, RAG pipelines or AI workflow tooling Strong stakeholder management and workshop facilitation skills Experience working in Agile product environments Commercially minded product thinking with a focus … SaaS, fintech, professional services or regulated environments Exposure to AI-enabled products, automation tools or GenAI initiatives Understanding of technologies such as LLMs, NLP, RAG pipelines or AI workflow tooling Strong stakeholder management and workshop facilitation skills Experience working in Agile product environments Commercially minded product thinking with a focus ...