Public repositories, published work, and production systems I can describe without exposing the private parts.
Systems I can describe publicly, with the confidential details left where they belong.
Internal systems for real-time analysis, portfolio review, market-data validation, and retrieval workflows used by front-office teams.
A LangGraph agent and evaluation pipeline for generating, checking, and improving TypeScript and Python unit tests in enterprise codebases.
Public work with source, write-ups, and enough technical context to make the tradeoffs inspectable.
A hardware-agnostic robotics operating system, shipped as a Python package.
A CUDA-kernel-free RWKV language model with MoE, LoRA, and windowed attention.
HMM and deep learning fusion for speech recognition on embedded hardware.
An end-to-end Azure ML pipeline that retrains itself weekly.
A local agentic assistant for drug information, evaluated on 1,000 prompts.
Zero-loss asynchronous messaging across a containerized service network.
Raft-backed metadata management for Kafka brokers, topics, partitions, and failover.
Smaller experiments and coursework remain on GitHub. This page is the work I would be happy to walk through in an interview.
