Incoming Software Engineer at Runpod (January 2027). Finishing my MS in ECE at Purdue in December. I work on LLM inference and data infrastructure.
- emberserve: LLM inference engine from scratch. Paged KV cache, prefix caching, continuous batching, OpenAI-compatible server. Matches vLLM throughput on the same GPU, and cold-starts in 17 s on Runpod Serverless vs ~130 s for worker-vllm.
- worker-vllm #351 (merged): persists vLLM's torch.compile cache on the network volume, taking 40–60 s off Serverless cold starts. Shipped in v2.29.0.
- serverless-lakehouse: medallion pipeline over my Runpod Serverless data. Built on Spark + Delta, ported to Snowflake + dbt with all 8 gold tables matching row for row, with the Runpod API as a live source. Dashboard
- Pulse: local-first news and market monitoring desktop app, ~50k lines of TypeScript (Electron, React, SQLite) with local and hosted LLM scoring.
- Aether: multi-agent personal AI platform with voice control, signed messages between nodes, and graph-based authorization.
Python · TypeScript · SQL · PyTorch · vLLM · Spark · Delta Lake · Snowflake · dbt · Docker · Kubernetes · Runpod Serverless
Previously Platform Developer at E&J Gallo.



