I build LLM-powered systems the way security tooling has to be built: the model gets a voice, never a vote. Eighteen years shipping production code at Walmart Global Tech, and a year since on self-directed R&D into making generated code safe to trust.
Open to AI engineering roles — contact below.
DevSecOps Sentinel — a GitHub Actions supply-chain analyzer where deterministic rules decide what is true and a schema-constrained model explains it. Try it live — no signup, nothing to install.
The part I'd want you to look at: the test suite includes a workflow whose comments tell the model to invent a finding and suppress a real one — the prompt-injection case, since scanned content is attacker-controlled — plus a recorded reply where the model obeys. The reply is rejected anyway, because a rule id the scanner never produced cannot survive the containment gate. Weaken that gate to a count comparison and 5 of its 14 containment tests fail.
The rules are scored against a golden corpus written independently of the code, and the whole eval runs offline in CI — no API key, no network, no spend. Scanned against 564 workflows from 14 major OSS repositories: 100% parsed, 94% carried findings, and two Critical rules returning zero across every file alongside 796 unpinned-action hits — a rule set discriminating, not spraying.
WidgetWorks — an e-commerce store built to a production security posture: JWT with rotating refresh tokens, TOTP 2FA, Google sign-in, atomic stock reservation, server-side re-priced checkout, pluggable payments. Live store — demo accounts for all three roles are on the landing page.
- BI-Simulator — the "bring 18 data sources together" problem, done agentically twice over: an AI agent built the pipeline (messy SQLite/CSV/JSON/JSONL/XML exports conformed, joined, flattened into one analytical model with a live cross-filtering dashboard), and an LLM now runs inside it — mapping an unseen 19th source from a closed transform vocabulary, landing only past eleven deterministic gates, negative-case tested, prompt-injection canary included. Deterministic by seed, replayed by CI on every push. Try it live · the evidence
- ClaudeChessApp — chess fully client-side: Stockfish, clocks, 2,969 World Championship games, a SQLite library, no backend at all, try it live
- Net10Sudoku — Blazor generator/solver, try it live
- LotteryApp — Powerball & Mega Millions checking against 4,493 real drawings — Mega Millions back to 2002, Powerball to 2010 — .NET 10 + Dapper, try it live
- ToDoApp — Clean Architecture + CQRS kanban with revocable JWT auth, React SPA over a .NET 10 API, try it live — demo account is on the sign-in page
Everything above is .NET 10 / C# — with React front ends on ToDoApp, WidgetWorks and Sentinel, and Angular on LotteryApp — except ClaudeChessApp, which is React and TypeScript with no backend at all, and the BI simulator, which is deliberately dependency-free Python. All of it is CI-gated: CodeQL across the board, dependency review on every repo with dependencies to review (the BI simulator, stdlib-only, has none), and secret scanning everywhere — gitleaks in CI on five repos, GitHub push protection on Net10Sudoku and the BI simulator. Deployed on free tiers — Azure and GitHub Pages — and built in collaboration with AI agents under the constraint the flagship demonstrates: generated code ships only after deterministic checks say it may.
Open to AI engineering roles — building LLM-powered systems, or putting guardrails around the ones that already exist.