I build practical AI automation: multi-agent systems, LLM workflows, and pipelines that turn manual work into reliable software. Based in Minneapolis. Background in TripleTen Data Science and Nucamp DevOps.
π― Open to: AI Automation Engineer / AI Agent Engineer roles (remote, US)
- Languages: Python, TypeScript/JavaScript
- Agents & LLMs: OpenAI API, Claude, LangGraph / LangChain
- Backend: FastAPI, Pydantic, Uvicorn
- Automation: n8n, GitHub Actions
- Media: faster-whisper, FFmpeg
- Quality: golden-case evals, regression gates, prompt-injection tests
| Project | What it does |
|---|---|
| seller-support-agent | LangGraph multi-agent seller support: classify β tool call β draft β confidence gate, with prompt-injection evals and CI. |
| whatnot-automation-agent | FastAPI multi-agent ops backend for a Whatnot business: inventory, orders, sourcing, and profit agents. |
| agent-eval-harness | CI eval harness for LLM agents: golden cases, accuracy/latency/cost scoring, and regression gates on every PR. |
| whatnot-sourcing-agent | AI sourcing pipeline that scans resale marketplaces, estimates margin after fees, and alerts on high-profit deals. |
| facelessIG | Speech-to-Shorts pipeline: faster-whisper transcripts β clip candidates β FFmpeg 9:16 renders via GitHub Actions. |
| prompt-engineering-portfolio | Prompts treated like code: versioned prompts, attack suites, and evaluation rubrics. |
- GitHub: @becominggiant