About
I spent my first six years as an engineer where most of the hard problems live — behind the interface. Designing API architectures that serve hundreds of thousands of requests, building queue-based ingestion systems that compress 30-minute ETL jobs into 2-minute pipelines, and deploying production infrastructure on bare-metal VPS when managed services weren’t an option.
I don’t just write endpoints. I design the data flow, the failure modes, and the recovery paths.
At Northeastern, I turned that operational intuition toward ML and distributed real-time systems. I built NLP classifiers for clinical urgency detection on messy, imbalanced EHR data. I architected a server-authoritative multiplayer VR environment where terrain state synchronizes across Meta Quest headsets at frame-level precision. I designed a cross-chain analytics platform that ingests and indexes 17,000+ decentralized applications through concurrent Redis-backed pipelines.
The thread across all of it: I build systems that handle complexity at the infrastructure layer so the product layer can be simple. Whether that means a BullMQ pipeline with dead-letter recovery, a Netcode authority model that prevents client-side state drift, or a vector similarity engine that returns semantically relevant results in under 50ms.
Capabilities
Where most of my production work lives. API design, data modeling, authentication, queue-based processing, and service decomposition — owning the full backend from schema migrations to reverse proxy config.
Six years of React in production — from D3-powered analytics dashboards to enterprise micro-frontends. I build interfaces with the same rigor I apply to backend systems: typed, composable, maintainable.
Built during my MS and beyond — text classification, embedding-based retrieval, and semantic feedback systems. ML that actually ships: proper evaluation, class imbalance handling, and deployment paths that work in production.
Comfortable operating production systems without managed service dependencies — Docker, Nginx, CI/CD pipelines, and process management on bare-metal VPS. AWS-certified for when the cloud makes more sense.
How services decompose, where state authority lives, how failure propagates. Applied directly: VR server authority models, BullMQ job orchestration, CI/CD pipeline isolation, and service decomposition in enterprise platforms.
Multiplayer VR state synchronization at 72Hz across N Quest headsets, live leaderboards with WebSocket broadcasting, and terrain streaming over deterministic frame buffers. Low-latency as a hard constraint, not an afterthought.
Direction
End-to-end product engineering, from data model to UI
Service decomposition, consistency tradeoffs, scalable architecture
NLP, semantic systems, embeddings in production
API design, pipelines, infra that teams rely on