12 of 12 Retrieval-Augmented Generation Jobs in the South West

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 ...

Integrate frontier and self-hosted LLMs (Claude, GPT, Gemini, open-weight models) with tools, data, and external systems through MCP and custom connectors. Apply RAG techniques where they actually help: vector databases (Pinecone, Chroma, Weaviate, pgvector), hybrid retrieval with Elasticsearch or Solr, and BM25 + similarity search. Work ...

modern vector databases. An understanding of Site Reliability Engineering best practices and Platforms/DevOps best practices. Exposure to AI concepts such as LLMs, RAG, and agent‐based systems (or a strong desire to learn). Experience previously working with central government and an understanding of government ways of working ...

Mistral, or similar) • Familiarity with API development and modern software architecture • Understanding of prompt design and managing AI-generated outputs • Ideally have experience in RAG pipelines and vector databases (e.g. Pinecone, Weaviate, pgvector) • Familiarity with cloud platforms is advantageous (AWS, Azure or GCP) • A proactive mindset with the ability ...

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 ...

experience with Docker, Linux/Unix CLI, Git, and testing Understanding of modern AI approaches, including: Agentic AI systems RAG (Retrieval Augmented Generation) Graph-based approaches Computer vision, audio, or multimodal models Knowledge of data governance and security best practices Strong communication and problem … solutions in secure environments High-performance computing and cloud-native AI architectures End-to-end MLOps lifecycle and model management Advanced AI techniques including RAG, agentic systems, and multimodal models Secure, high-assurance data environments Modern DevOps, CI/CD, and engineering practices Why Join? Work at the forefront ...

delivering innovative machine learning solutions: Building and deploying LLMs and ML models (cloud or HPC) Leading technical delivery across projects Driving adoption of GenAI, RAG, and agent-based systems Acting as the SME for ML engineering and deployment Tech Focus Python, Docker, Linux, Git AWS or Azure CI/… MLOps pipelines Exposure to RAG, computer vision, or multimodal AI Why Apply? Work on next-generation AI use cases Strong focus on innovation and career progression Opportunity to lead and shape technical direction Security clearance eligibility required. ...

seeking an exceptional candidate to join our dynamic team as a Principal Research Engineer or Research Fellow to lead the development of next generation technologies and strategies for distributed data sharing and integration of systems. This role focuses on innovative solutions leveraging cloud and edge technologies, enhancing data … tools such as Matlab Simulink (e.g. optimisation and ML toolboxes), PySpark and Jupyter notebooks. Work on projects on areas such as agentic AI, MCP, RAG, generative AI, trust and security, driving innovations from concept through PoC to MVP. Experience with semantic web technologies; RDF, OWL, knowledge graph and ontologies ...