5 of 5 Retrieval-Augmented Generation Jobs in Scotland

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 ...

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 ...

Python. What you'll be doing Designing and building production LLM and GenAI solutions in Python from prototype through architecture to deployment. Implementing advanced RAG pipelines hybrid search, re-ranking, query rewriting, context compression, evaluation and grounding. Working with vector stores such as Azure AI Search, Pinecone, Qdrant, Weaviate … production-ready code. Hands-on experience building and deploying LLM/GenAI solutions in production not just POCs or notebooks. Strong working knowledge of RAG patterns, agentic frameworks, vector databases and embedding models . Familiarity with prompt engineering, LLM evaluation and guardrail design (hallucination mitigation, prompt injection defence, content safety ...

Data & AI platforms (Azure AI, Vertex AI, Databricks, Hugging Face), advanced GenAI frameworks (LangChain, HF Transformers, LlamaIndex) & Agentic architectures (LangGraph, SmolAgents, PydanticAI) Vector DBs & RAG: Designed solutions using vector DBs & Retrieval Augmented GenAI (RAG) for knowledge applications. GenAI ModelOps/MLOps & Governance: GenAI ModelOps/MLOps ...