Most AI work fails not at the model but at the engineering around it: data, evaluation, cost control, and shipping something that holds up in production. That is where my background helps, since I have spent years building reliable data pipelines and full-stack systems.
I can take an AI feature or agent from idea to a shipped, evaluated, cost-aware product, and I am honest about where AI helps and where it quietly adds risk.
What I bring
- AI features and agents wired into real products, not demos.
- Evaluation, cost control, and reliability around LLMs.
- Full-stack engineering foundation in Go, TypeScript, Node, and React.
Proof, not promises
Real work, written up plainly: