AboutSan Francisco
I build LLM systems end to end
I am a software engineer working on applied AI. I build the systems that sit around machine learning models: the pipelines that feed them, the agents and products built on them, the evaluation that checks their output, and the infrastructure that keeps them running. Robotics is where I trained; applied AI is where I work.
Where the instinct comes from
My background is in robotics. I spent my master's at the University of Maryland working on perception and control, where a system that is confidently wrong does not fail quietly. That experience shaped how I approach machine learning: I am less interested in whether a system produces an answer than in whether we can tell when the answer is wrong.
The coursework is still on GitHub and I have kept it on this site, honestly labelled: a TurtleBot perception stack whose horizon comes from a hand-written RANSAC vote, planners whose termination rules can be shown sound or unsound, a YOLO stack on a live ROS 2 topic. None of it is polished software. All of it is where the habit of asking "how would we know?" started.
How I work
I work across the full stack, largely because the problems worth solving rarely stay in one layer. A slow page turns out to be a missing database index; a model that appears accurate turns out to be a pipeline that stopped running. Diagnosis is the part of the work I find most rewarding.
I place particular value on the engineering that is easy to skip: tests that genuinely fail when the code breaks, alerting that fires when it should, and migrations that can be rolled back safely.
A test only counts once a deliberate defect in the source proves it can fail, and the defect itself is verified as landed. Tests that pass against broken code are worse than no tests: they transfer confidence without earning it.
The path
- Jul 2019 to Jun 2023 SRM Institute of Science and Technology B.Tech, Mechatronics, robotics specialization Control systems, embedded systems, sensors and signal conditioning.
- Jul 2023 to May 2025 University of Maryland, College Park M.Eng, Robotics Perception for autonomous robots, planning, control, reinforcement learning. Robotics lab
- Sep 2025 to May 2026 Handshake AI AI Data and Evaluation Calibrated rubric evaluation of frontier image models; the human side of the evaluation pipelines I now automate. Case study
- Apr 2026 to present Verita AI Software Engineer, San Francisco Production LLM systems: audit pipelines, an annotation platform core, a rubric verifier, SOC 2 readiness, security tooling. Case study
Sources Résumé Verified 3 Sep 2026