Building AI-powered products customers trust and businesses value.
I translate complex enterprise workflows into scalable products by connecting customer insight, product strategy, responsible AI thinking, analytics, and cross-functional execution.
Strategy, requirements, execution, product health, and measurable outcomes.
Evidence
Impact anchored in product context
Each metric links to the product story behind it, with team outcomes clearly separated from individual contribution.
Years across product, business analysis and enterprise delivery
Explore the supporting case studyRoadmap alignment across engineering, UX, sales and support
Explore the supporting case studySurveillance cameras in a city-scale public-safety program
Explore the supporting case studyCommunity cameras connected to the broader ecosystem
Explore the supporting case studyProduct dashboards and reporting experiences designed
Explore the supporting case studyArtwork revision-cycle improvement associated with GLAMS process changes
Explore the supporting case studySelected work
Product stories across AI, scale, finance, and regulation
Four case studies show how I approach ambiguity, stakeholder alignment, workflow design, responsible AI, and product execution.
Shaping AI-Enabled Workflows for Enterprise SaaS
Evaluating AI opportunities and translating them into responsible, measurable enterprise product requirements.
Translating Computer Vision into City-Scale Public-Safety Workflows
Converting complex operational and Computer Vision use cases into coordinated workflows, dashboards and requirements.
Improving Visibility in Construction Finance
Clarifying complex construction-finance workflows through structured requirements, prioritization and reporting.
Streamlining Regulated Artwork Approval
Supporting structured approval, document-control and change-management workflows in a regulated environment.
AI product leadership
Applying AI where it creates accountable value
AI is a product system—not a feature label. Workflow, data, experience, evaluation, human oversight, economics, and operating reality must work together.
AI product system
Useful
The capability must improve a meaningful user task, workflow, or decision.
Dependable
Quality thresholds, failure behavior, privacy, and oversight must be explicit.
Human-centered
Review, correction, escalation, and accountability belong in the experience.
Sustainable
Adoption, latency, operating cost, and business value must support production scale.
Career evolution
From requirements quality to AI product leadership
My foundation in requirements, UAT, governance, and enterprise delivery continues to shape how I lead products today.
Senior Product Manager · nGenue
AI-enabled enterprise SaaS strategy, roadmap governance and product-health metrics.
Product Manager · Onpassive
Cross-functional execution, UX partnership, BA mentoring and executive reporting.
Product Manager · Vaco Binary Semantics
B2B SaaS requirements, prioritization, dashboards and construction finance.
Product Owner · Iridium Interactive
Smart City, Computer Vision and AgriTech product ownership.
Product Business Analyst · Perigord
Regulated Life Sciences workflows, UAT and change control.
Senior Business Analyst · NebuLogic
Requirements engineering, quality governance, UAT and change management.
Built across complexity
Experience spans enterprise SaaS, AI-enabled workflows, Smart City, construction finance, regulated Life Sciences, analytics, and multi-stakeholder delivery.
Let’s build products that matter.
I’m interested in Senior Product Manager and AI Product Manager opportunities where product judgment, enterprise execution, and responsible AI thinking matter.