Retrieval-Augmented Generation (RAG) is revolutionizing AI by combining the power of retrieval and generation, enabling models to fetch real-time, relevant information before crafting responses.
We're looking for RAG engineers to push the boundaries of AI-driven search and knowledge synthesis. If you’re passionate about vector databases, embeddings, and optimizing retrieval pipelines, join us in building the next-generation intelligent systems!
