AI platform and LLM product leader turning ambiguous bets into shipped systems.
I work at the intersection of technical program leadership, product strategy, and hands-on AI delivery. My focus is building the operating systems around AI products: clear requirements, dependable evaluations, observable workflows, risk controls, and execution paths that teams can actually run.
Problem: AI programs lose execution clarity when PRDs, ticket dependencies, risks, and leadership decisions live in disconnected artifacts.
Key decisions: built a contract-first, deterministic core before adding generative enrichment; rejected unknown/cyclic dependencies; kept outputs draft-by-default behind an explicit human approval gate; recorded run metrics and trace evidence.
Proof: executable Python CLI plus a browser-native compiler with JSON upload, artifact preview, approval-state control, and six-file ZIP export. A read-only Jira Cloud adapter, provider-neutral review-only LLM enrichment boundary, and OpenTelemetry-compatible spans show real integration and observability decisions. Ten automated tests and a 5/5 evaluation gate pass across Python 3.10–3.12. The reference input produces all six artifacts at a measured 0.24 ms median / 0.36 ms p95 over 100 local runs, with zero core runtime dependencies and $0 deterministic model cost. The repository includes least-privilege workflows, an ADR, threat model, sanitized case study, and a live interactive demo.
Problem: useful decisions and technical context disappear inside linear AI exports.
Key decisions: separated deterministic and model-backed work into nine bounded stages; added bounded concurrency, retries, checkpoints, local-first processing, and configurable model/cost controls.
Proof: Python 3.10–3.12 CI, a mocked-API integration test across every stage, 27% total coverage with 72% on orchestration, a synthetic sample vault, ADR, and a reproducible 100-run offline benchmark. The integration test also exposed and prevented a real final- statistics type error before v1.1.
Problem: product teams often make launch decisions from demos instead of repeatable quality evidence.
Key decisions: built a provider-neutral JSONL contract with transparent required/ prohibited-term scoring, versioned datasets, machine-readable reports, and non-zero CI exit codes for regressions.
Proof: runnable Python package with single-run and named-run comparison CLIs, 11 tests, 87% branch-aware coverage, a five-case product-support regression set, human-review calibration with MAE/bias reporting, a Python 3.10–3.12 launch gate, and a live evidence-boundary dashboard.
Problem: engineering status reporting wastes time and often loses commit-level evidence.
Key decisions: kept the workflow local and deterministic, derived summaries from Git history, and removed an unverifiable npm-registry claim until a publish token exists.
Proof: executable Node CLI, four automated tests, linting, zero known npm audit vulnerabilities, and CI across Node 18, 20, and 22.
- Program Management Skills — reusable launch, strategy, stakeholder, prioritization, and postmortem playbooks
AI platforms Agentic systems LLM evaluations
Product strategy Technical programs Production operations
I am especially interested in roles where AI platform strategy, cross-functional execution, and technical depth matter equally.