At Meta, I lead AI/ML infrastructure capacity data initiatives for scalable machine learning platforms, high-throughput workloads, and production inference systems. I design distributed services for request routing, workload scheduling, resource allocation, latency reduction, and reliability.
I've built ML serving architectures with containerized deployments, automated scaling, cloud-native infrastructure, and observability through metrics, logging, tracing, and operational dashboards. My work includes optimizing GPU utilization, capacity requirements, and performance bottlenecks across large-scale AI systems.
Previously, I managed data engineering at Meta and built distributed data processing systems for Messenger, Instagram, enterprise analytics, and machine learning workflows. I use Python, SQL, Kubernetes, cloud platforms, and performance engineering to make production systems more reliable and efficient.
