I work at the intersection of distributed systems, AI, and data infrastructure, turning complex technology into things developers can understand, adopt, and build with.
Lately, that means hands-on AI engineering: building vector search, semantic caching, agent memory, and RAG into the data layer; contributing to LangChain4j and RedisVL for Golang; and figuring out how to make AI agents secure enough to ship.
The AI-native work isn't a pivot. It draws on the same systems-design foundation I've built for 15 years, moving data fast, at scale, close to compute, watching where systems break; now applied to vectors and agents. I've worked on event streaming with Apache Kafka and Flink at AWS and Confluent; observability at Elastic; and RDBMS, NoSQL, and Big Data at Oracle. That foundation is exactly what separates AI demos that work on stage from AI systems that survive production.