Data, Software & AI Engineering
This might be you...
> You have an AI idea—or even a working prototype—that looks promising, but turning it into something reliable, scalable, and maintainable feels murky.
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> You want to move quickly, yet hiring a full team or committing to a large build feels risky, especially when the underlying data, infrastructure, or system boundaries aren’t fully clear.
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> The demo works. The real question is whether it will hold up once customers, uptime, or regulatory constraints start to matter.
Building AI is easier than ever.
Making it production-grade is not.
Most teams hit the same wall: the demo works, but everything underneath it is brittle. Data pipelines aren’t designed for change, system boundaries are fuzzy, and operational concerns show up late—usually under pressure.
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The difference between a convincing prototype and a production system isn’t a better model. It’s the unglamorous first-principles work that keeps things from breaking once real data, real users, and real failure modes enter the picture.
How I work
I work hands-on with a small number of teams at a time, focusing on technical direction, core system design, and execution that holds up in production.
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The role usually spans strategy and implementation: helping weigh tradeoffs, make consequential decisions, then building or refactoring the systems that those decisions depend on.​
Engagements are usually multi-month and deeply hands-on.
Case Studies
These were moments where a wrong technical decision would have been expensive to unwind...
> Shipping a HealthTech AI assistant, under constraints​
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A founder had a clear AI concept, but no safe path through HIPAA, data strategy, or system design. I narrowed the problem, designed compliant foundations, and shipped a production-grade assistant to help real people, instead of an expensive prototype that wouldn’t survive scrutiny.​​​​
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> Scaling an AI system past its breaking point​
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An experienced AI team had a working system that couldn't scale and was difficult to debug. I rebuilt the data pipeline and software infrastructure, enabling 50× scale while cutting marginal costs by roughly 90%.​​​
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