RAG Systems Engineer

RAG (Retrieval-Augmented Generation) Systems Engineers design and build knowledge retrieval pipelines that ground large language model responses in accurate, up-to-date information from enterprise knowledge bases and documents. They architect document chunking strategies, embedding pipelines, retrieval ranking systems, and context injection mechanisms, measuring and improving answer accuracy, relevance, and latency. This is a high-demand specialty within AI engineering as enterprises adopt LLM-powered knowledge management systems.