8 of 8 Permanent Retrieval-Augmented Generation Jobs in Tyne and Wear

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 ...

Responsibilities Architectural Leadership & Integration Design and implement enterprise-grade AI platforms - including semantic search, Retrieval-Augmented Generation (RAG), and broader generative AI/agentic capabilities - integrating cleanly with the department's complex Legacy and multi-cloud environments. Responsible AI Governance Embed … team. Alongside the DDaT competencies, given the scale of this programme, hands-on depth across the modern AI engineering stack is essential, not just RAG in isolation: Semantic search, vector/embedding infrastructure, and retrieval architecture design LLM orchestration and agentic frameworks (eg LangChain/LlamaIndex-style tooling ...

Integrating AI capabilities into applications and workflows Developing and evaluating LLM-based solutions Building Retrieval-Augmented Generation (RAG) systems Testing, debugging, and improving non-deterministic AI systems Evaluating AI performance, limitations, and risks Applying machine learning concepts to solve complex problems What theyre looking … Experience with Python and AI/ML technologies Understanding of LLMs, machine learning concepts, and statistical methods Experience with RAG, model evaluation, or AI applications Strong analytical thinking and problem-solving ability Interest in responsible and secure AI development Experience in defence, national security, AI safety, or constrained environments would ...

Model programme for a central government department. Where the Lead Technical Architect sets direction, you turn it into working, production-grade systems - semantic search, RAG pipelines, and broader generative AI capability - integrated into complex Legacy and multi-cloud environments handling high-volume, sensitive public sector data. You'll work inside … eventually run these systems without long-term reliance on external suppliers. Key Responsibilities Hands-On Build & Delivery Design, build, and ship production AI components - RAG pipelines, retrieval and embedding infrastructure, orchestration logic, and integration layers - writing high-quality, tested, maintainable code and staying close to implementation rather than ...

Forward Deployed Engineer programme. Key Responsibilities Architect and govern production‐grade agentic systems at enterprise scale: multi‐agent orchestration across complex environments, RAG pipelines, policy‐based routing, memory management, and programme‐level lifecycle observability Define RAG pipeline standards across engagements: establish chunking and embedding strategies, set quality benchmarks, and ensure … depth, not tutorial level Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering LLMOps fundamentals: eval harness design, prompt versioning, and production observability Cloud‐native engineering maturity: Kubernetes ...

Team" alongside civil servants, implementing the guardrails and standards set by the Lead AI Engineer and Lead Technical Architect. Key Responsibilities Build & Integrate Implement RAG pipelines, prompt templates, and agentic workflows, and wire them into application logic that connects with the department's Legacy and multi-cloud systems. Prompt Engineering … internal government engineers. Required Skills & Experience Practical experience building applications using LLMs or other foundation models, via API or self-hosted Experience with RAG architectures, vector databases, and embedding-based retrieval Solid general-purpose programming (commonly Python) and API design/integration skills Prompt engineering with a systematic ...

Lead Agentic Engineer (MedTech) We’re partnering with an innovative company in the North of England that is building next-generation intelligent solutions. They are now looking for a Lead Agentic Engineer to help design, build and deploy AI-powered software solutions that will support clinical and operational … scalable design Experience building modern web applications and production software Experience working with LLMs, AI-powered applications or agentic workflows Understanding of prompt engineering, RAG pipelines and tool-using AI systems Experience integrating APIs, data sources and cloud platforms (AWS, Azure or GCP) Bonus experience: Experience with AI agent frameworks ...