I work at the intersection of AI, data infrastructure, and distributed systems. In practice, that means hands-on AI engineering: building vector search, semantic caching, agent memory, and RAG into the data layer, and figuring out how to ship AI agents securely. I also contribute to the LangChain4j open-source project.
The AI-native work isn't a pivot, though. It draws on the same systems-design foundation I've built for 20+ years: designing data systems for scale, processing fast data, watching where systems break; now applied to vectors and agents. I have worked on RDBMS and Big Data at Oracle; event streaming with Apache Kafka and Apache Flink at Confluent; observability at Elastic; AI and developer tooling at AWS; and NoSQL and vector stores at Redis.
This foundation is exactly what separates AI demos that work on stage from AI systems that survive production.