6 of 6 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 ...

technologies Experience architecting scalable AI and ML solutions across serverless, containerised or GPU enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience ...

working across: Microsoft Azure (including AI services/Foundry) .NET and modern backend engineering APIs, microservices and DevOps practices GenAI/LLM tooling (RAG, prompt engineering, embeddings, vector search) This is a role for someone who enjoys solving challenging problems and building robust, real-world systems in a fast-moving ...

multiple levels, from Engineers through to senior technical leadership. What you'll be doing Building AI-enabled platforms and intelligent workflows Working with LLMs, RAG pipelines and embedding models Developing scalable backend services and AI integrations Collaborating across engineering, architecture and business teams Supporting AI deployment, governance and operational rollout … Environment Python, SQL LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases, FastAPI, APIs AWS, Azure, GCP or Databricks CI/CD, MLOps and LLMOps Requirements Strong software or data engineering foundations Experience delivering AI or AI-enabled solutions Exposure to cloud and modern data platforms Financial ...

Hands-on experience with LangGraph, LangChain, LlamaIndex, or CrewAI building multi-step agentic systems RAG & knowledge systems: Practical experience designing and building retrieval-augmented generation pipelines AI evals and observability: Experience building evaluation frameworks and using tools such as LangSmith to monitor and constrain ...

doing Designing AI and GenAI architecture strategies and roadmaps Building scalable AI/ML and Agentic AI platforms Working across LLMs, RAG, embeddings and semantic search Supporting AI deployment, governance and integration patterns Collaborating with engineering, data, DevOps and business teams Advising clients on AI platform capability and implementation strategy … Environment Python LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise AI or ML architectures Strong understanding of cloud and modern data platforms Exposure to scalable AI deployment ...