Quick answer: Retrieval-augmented generation specialist — vector DBs, embeddings, retrieval + ranking.
A RAG Engineer specializes in building systems that combine large language models with external knowledge sources to deliver accurate, grounded answers. Rather than relying solely on a model's training data, RAG (Retrieval-Augmented Generation) engineers design pipelines that fetch relevant information from vector databases, documents, or APIs in real-time, then feed it to LLMs for contextual responses.
In India, RAG engineers are increasingly sought by fintech companies like Razorpay and Swiggy, government digitalization initiatives under NeGD, and AI-native startups building LLM applications. As enterprises move beyond generic chatbots to specialized domain applications—legal document review, customer support, medical knowledge bases—RAG expertise becomes critical. The role sits at the intersection of backend engineering and machine learning, requiring comfort with both infrastructure and embeddings.