Kumar Shivendu is a Software Engineer on Qdrant’s core team, where he builds distributed systems for vector search at billion-scale. He was an early engineer at Qdrant and has worked on storage, replication, consensus, sharding, and infrastructure that powers large-scale search deployments. He’s particularly interested in information retrieval, distributed databases, AI agents, and the intersection of search and generation. Outside of Qdrant, he writes and speaks about search systems, databases, and “napkin math” for understanding large-scale infrastructure.

In 2012, Doug got bit by the search bug and he's still trying to keep up. From full-text search, to Learning to Rank models, to search agents that generate their own code, he knows the endless landscape first hand. Yet Doug wants to deeply understand the what / how / why, and help teams use these technologies practically, distinguishing hype from reality.

He’s led search at Reddit, Shopify, and Wikipedia, authored Relevant Search and AI Powered Search, and advised 100+ organizations over the years - all in pursuit of the same question: how does search actually work?