Hands-on AI and agentic work
Building and evaluating AI workflows, advising teams, and translating fast-moving capability into product and operating choices.
I learn AI by building with it.
Then I translate the build into the decisions a leader or team can actually use: what the system can do, what it needs, where it fails, and what would make it trustworthy.
Current work first
Generative AI, agentic workflows, and the operating layer between prototype and practice.
My work combines hands-on AI prototyping and evaluation with product thinking, enterprise data experience, and a bias toward teaching from visible artifacts. The topic may be agentic coding, A2A, an AI product workflow, or the controls around it. The method stays consistent: build, test, explain, and label the evidence honestly.
The background underneath it
Product leadership and enterprise data work shape what I notice.
Marketplace and product leadership
Work across marketplace product strategy, P&L ownership, experimentation, conversion, customer analytics, and AI transformation.
Enterprise data and platform work
Work across enterprise data migrations, reporting and analytics, data quality, data standards, regulatory controls, security, audit, and remediation.
What I believe
The best AI education does not make the technology look magical. It makes the work legible.
That means showing the source, naming the test, keeping the failure, and separating an artifact from a deployment or an outcome.
Work together
Bring one real AI workflow.
We’ll find the decision, evidence, and operating practice it needs.
Discuss your workflow