AI Engineer building reliable RAG, multimodal, and voice systems in Python.
I build the retrieval, context, provider-integration, evaluation, and backend reliability layers that turn model capability into useful systems.
- Applied AI: grounded retrieval, multimodal inputs, voice workflows, structured outputs, and measurable evaluations
- Reliable services: async Python, FastAPI, retries, caching, webhooks, and idempotent workflows
- Product systems: privacy-aware desktop apps, provider routing, explicit control boundaries, and automated verification
A macOS AI interview and coding copilot with screen and voice context, provider routing, secure native components, and best-effort capture exclusion.
Local-first RAG with adaptable CSV mapping, recoverable indexes, validated citations, and a measured semantic-vs-BM25 evaluation.
Source · Architecture · Results
Leakage-resistant temporal ML evaluation with calibrated models, frozen holdouts, and honest bookmaker comparison.
Eight merged upstream contributions, selected for relevance to agent reliability, evaluation, tracing, and framework compatibility:
- OpenAI Agents SDK #3991: bounded retry behavior for retryable pre-response WebSocket failures
- Pydantic AI Harness #503: preserved model retry context after host-side Code Mode session resets
- Mellea #1471: deterministic tracing coverage for async spans, context propagation, and token usage
- FastStream #2961: FastAPI 0.140 compatibility with preserved AsyncAPI metadata
View four additional merged contributions
Primary: Python, FastAPI, asyncio, Ollama, Chroma, scikit-learn, XGBoost
Product and delivery: Electron, Swift, React, Docker, GitHub Actions, Playwright, n8n, Azure AI Foundry
Building VolyxAI and independently developing Volyx Lens. Open to AI Engineer and ML Engineer opportunities.