AI engineer — open to full-time and contract.

I design, build, and ship production AI systems end to end — agents that do real work, retrieval that returns the right thing, evals that tell you whether it actually works, and LLM features that live inside real SaaS. I care as much about the parts that aren't glamorous — the eval harness, the failure analysis, the fallbacks — as the demo.

Below is the evidence, in the order I'd want you to weigh it: the code first, then shipped products, then the thinking behind them.

Read the code

The fastest way to evaluate me is to read what I've written. My flagship open-source artifact is text-to-sql-prompting — Measuring the accuracy lift of each prompting technique on text-to-SQL — execution-grounded evals, honest failure analysis. It's the readable AI-engineering code behind the positioning: a prompting ladder measured against execution-grounded evals, with an honest write-up of where it still fails.

More work lives on my GitHub profile.

Selected work

Three shipped projects that show the range — real LLM integration, a production security audit, and a live multi-tenant SaaS with daily users:

• Fetti — Money Tracker for Cash Workers — receipt OCR on Anthropic vision. Receipt OCR built on Anthropic vision — snap a photo, get a structured, draft expense entry. Live at fetti.tips.
• Justice Ledger — Legal Practice Management SaaS — dual Anthropic + OpenAI legal research. Dual Anthropic + OpenAI SDK integration for legal research and document summarization, plus a full security audit of 32 data-touching routes.
• PowderLedger — Ski & Snowboard Rental Management. Production multi-tenant SaaS in active daily use at a real rental shop — the depth of a system someone else can run and extend.

Writing

How I think about building with AI — the trade-offs, not the hype:

• ChatGPT vs Claude vs Gemini vs Copilot vs Meta AI: A Developer's Honest 2026 Comparison
• Your AI Agent Forgets and Degrades — Engineering Context Across Time
• AI Writes 1.7× More Bugs. Two Disciplines Decide Whether They Ship.

How I work on a team

I usually ship solo, but I work the way a team needs: clear specs, reviewable PRs, honest trade-off write-ups, and code someone else can pick up. I review my own work adversarially — and I document the decisions the AI couldn't make, so the next person inherits the reasoning, not just the diff.

Let's talk

If you're hiring for AI engineering work — full-time or contract — I'd like to hear about it.

Get in touch →