Frontier AI systems for high-trust deployment.

I’m an AI/ML engineer at Google working on Gemini. I build at the systems layer between model capability and deployed value: infrastructure, evaluation, reliability, interfaces, and operational trust.

The next generation of AI work will not be won by benchmarks alone. It will be won by people who can turn frontier models into systems that survive contact with real constraints.

That is the work I am orienting around.

Direction

I am drawn to work where technical depth and product judgment compound.

That means building systems with:

• hard AI systems constraints
• fast research-to-production loops
• real users and deployment pain
• high-trust workflows where reliability matters
• clear consequences when the technology fails

The goal is to become the kind of builder who can bridge model capability, deployment reality, and product judgment.

What I build

My public projects test the same thesis from different angles:

• ATO Copilot: source-backed AI for government authorization workflows. It explores how AI can help humans move through compliance and review by preserving evidence, provenance, and accountability.
• Bare-Metal AI Lab: local AI infrastructure for model serving, observability, and deployment experiments. It keeps me close to the operational layer below the API: GPUs, vLLM, telemetry, quantization, service replacement, and failure recovery.
• RFP Map: a mobile-first market intelligence radar for SAM.gov opportunities. It treats public procurement data as terrain: something to explore, cluster, and reason over.

The common thread is deployment reality.

Models are powerful. The hard part is building systems people can actually use.

Proof base

At Google, I work near frontier model systems through Gemini. Before Google, I built large-scale ML and cloud platforms at Capital One, including model-training infrastructure used by thousands of data scientists, machine learning engineers, and analysts.

My research background is in deep learning, computer vision, and medical image analysis. At Vanderbilt University, I developed new ML algorithms and published peer-reviewed research.

That background still shapes my engineering taste: models matter, but deployment, observability, data quality, and human trust determine whether they create value.

What I am looking for

I am most interested in people, teams, and problems that compound toward one of two paths:

1. Founder-track company creation: learning where frontier AI breaks in real workflows and where new infrastructure should exist.
2. Elite engineering: working with high-talent-density teams on hard model, infrastructure, deployment, and evaluation problems.

The best opportunities sit at the intersection.

Hard systems. Real users. Fast loops. High standards.

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