4 of 4 Retrieval-Augmented Generation Jobs in Northern Ireland

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 ...

have 'under-the-hood' knowledge of Python, specifically for building high-throughput microservices and complex data pipelines using FastAPI, Pandas, and NumPy . The RAG & Agentic Expert: This is the 'Critical' requirement. Our client needs someone with deep hands-on experience building Retrieval-Augmented Generation (RAG) pipelines and Agentic frameworks . You should know how to use LangChain or LlamaIndex to create AI that can execute multi-step tasks. The Data Architect: Proficiency in Vector Databases is essential. You should be comfortable designing data persistence layers using PG Vector, Pinecone, Milvus, or Mongo ...

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 ...