I help teams turn consequential software problems into clear, reliable systems—especially where architecture, integration, production behavior, and AI-enabled workflows meet.
I bring more than three decades of software development to problems that are expensive, ambiguous, or simply too important to get wrong.
That can mean shaping a new backend, untangling a brittle production workflow, making search behave under failure, or putting clear decision boundaries around an AI-mediated system. I begin by clarifying the problem, constraints, and ways a system can fail—then make responsibilities, interfaces, and operational signals explicit. The goal is not maximum ceremony. It is to engineer the system to work as well as necessary, and no more elaborately than the problem deserves.
Designed search-quality, failure-analysis, and autocomplete approaches for a global knowledge platform—keeping useful results available even under degraded, high-latency conditions.
Established service architecture for authorization, account verification, two-factor authentication, Plaid integration, and secure REST/OpenAPI patterns in a regulated workflow.
Built source-grounded regulatory intelligence and assessment workflows that preserve context, expose decision boundaries, and make consequential outputs inspectable.
If you need senior technical judgment alongside someone who still enjoys building the thing, email jim@scarb.us.