I build backend systems that other engineers depend on. Most of my work involves API infrastructure, data pipelines, and developer tooling—basically making sure things work reliably at scale and reducing manual operational overhead.
At Verizon, I built an API governance platform that processes 500+ OpenAPI specs daily for 800+ developers. The system validates specifications, generates documentation, and enforces standards—all running on AWS Lambda with deterministic re-runs and artifact versioning.
At Alana, I'm working on their product ingestion pipeline, which handles 2.5M-5M products per catalog. The existing process required 6+ hours of manual intervention (EKS scaling, shell access, manual Elasticsearch triggers). I redesigned it as an automated, idempotent workflow with proper state tracking and failure recovery.
I also work on ChatDBG at UMass PLASMA Lab—an open-source VS Code extension with 100k+ downloads that augments native debuggers for Python, C++, and Rust. I re-architected it from a CLI prototype to a full IDE integration using the Debug Adapter Protocol.
Python and TypeScript for most backend work. FastAPI and Django when building APIs. AWS (Lambda, S3, EKS), Docker, and Kubernetes for infrastructure. PostgreSQL, Redis, and Elasticsearch for data storage and indexing.
I care about things like idempotency, deterministic re-runs, proper failure handling, and observability—basically building systems that don't break in production and are debuggable when they do.