4 of 4 Retrieval-Augmented Generation Jobs in South Yorkshire

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 ...

/B experiments and performance dashboards. • Integrate AI agent functionality with applications through MCP servers and reliable tool configurations. • Develop secure and scalable RAG pipelines, including ingestion, text processing, metadata handling and vector search in GCP. • Embed security, compliance, GDPR controls and best‐practice engineering with clear documentation and stakeholder … Strong Python skills for backend services, SDKs, API development, authentication and testing. • Hands‐on experience working with LLMs, embeddings, prompt engineering, tool calling and RAG design. • Practical knowledge of GCP services including Vertex AI, Model Garden, BigQuery, Cloud Run and Google’s ADK. • Experience with document and data processing using ...

influence technical strategy, architecture, and best-practice from day one. THE ROLE: Build and operate production-ready AI agents on GCP Design robust RAG pipelines covering ingestion, text processing, vector search and secure data handling. Implement evaluation frameworks, monitoring, A/B testing and performance dashboards for latency, cost … Engineering or Software Engineering with applied AI delivery. Deep hands-on expertise with GCP services Strong Python skills Practical experience with LLMs, embeddings, RAG architectures and vector databases. A track record of building production-grade AI systems, including observability, evaluation and automated testing. DevOps capability across CI/CD, Docker ...

business. Key responsibilities include: • Designing and delivering an AI platform that supports large‐scale agentic automation and end‐to‐end claims journeys • Building robust RAG pipelines, LLM‐based systems, and deployment frameworks • Developing AI agents for claims handling, internal tooling, and customer‐facing chat solutions • Evolving an internal AI assistant … engineering or software engineering with a focus on LLMs and production AI systems • Expertise in deploying AI models and agents at scale, including RAG, embeddings, and LLM tooling • Hands‐on cloud engineering experience, ideally in GCP and its AI ecosystem • Strong Python engineering ability and experience building production‐grade services ...