AI · Data · Engineering
I bring AI, data, and actionable tool engineering to organizations that need it. I approach every project with a founder's mindset. Ship fast, validate early, double down on what works. Mobile apps, internal tools, LLM workflows. I turn ideas into things teams actually use.
I'm high agency, not high certainty. I lead by spotting potential in people and creating conditions for them to shine. Their wins are my wins.
I lead AI and data platform teams but think like a founder. I prioritize impact over process and remove friction so teams can focus. I don't show up with opinions. I show up with a case.
I architect and run data and AI systems end to end: streaming, vector DBs, embeddings, RAG, and LLM orchestration for mobile and web products. I build agents and tools that real people use, with logging, evaluation, and guardrails baked in from the start.
I write code in Python, run infra on Docker and AWS, and use Git workflows while keeping speed to market in mind. I also design and launch internal tools, mobile apps, and productivity products that help teams work smarter.
| Area | Stack |
|---|---|
| Languages | Python, SQL, some Go and TypeScript |
| Data & AI | Kafka, streaming, vector DBs, embeddings, RAG, LLM orchestration (OpenAI, Anthropic, Llama, local models) |
| Infrastructure | AWS, Kubernetes, Docker, Terraform, CI/CD, observability |
| Development | Git, GitHub Actions, testing, clean architecture, documentation |
| Mobile | React Native, Expo, mobile APIs, offline support for AI-powered apps |
I build things constantly. Most don't become businesses, and that's the point. They're R&D. Every project pushes my skills somewhere I haven't been before, which makes me sharper at the day job.
- Productivity tools and mobile apps that use AI to automate repetitive tasks or summarize information.
- LLM agents for internal teams. Support bots, ops helpers, eng accelerators.
- Data platform slices: minimal but realistic examples of ingestion, transformation, and serving for AI and analytics.
- Infrastructure patterns I reuse to spin up new experiments on AWS and Docker quickly.
| Shipping quickly but safely. | Prototype, learn, iterate. Perfection can wait. |
| Building things people actually use. | Real time saved, not demos. |
| AI that makes sense in context. | Models and agents that fit into existing workflows, not bolted on. |
| Simple, maintainable architecture. | Systems I can move fast with now without paying for it later. |
If you're working on AI-powered apps, internal tools, or productivity products that solve real problems, reach out.