Playbook
Product work isn't done when the ticket closes — it's done when people can use it and the next engineer can build on it.
Eight years of production engineering and a run of AI-native builds keep returning to the same idea: the interface is where the product meets the person, the AI is where it earns its edge, and the codebase is where it meets the next engineer. All three deserve product judgment. The playbook favors work that compounds — typed contracts, reusable systems, audited accessibility — over work that merely ships.
How I lead
Prototype to prove, then commit
Start with a Storybook or proof-of-concept to de-risk the idea, then build the funded version properly. Investment follows evidence, not slides.
Accessible and fast are requirements, not polish
WCAG compliance and performance belong in the definition of done — verified with axe-core and Lighthouse, not left as a later pass that never comes.
Own the whole feature
From the API contract and the AI pipeline to the rendered, accessible interface — a product engineer takes responsibility for the experience end to end, not just one layer of it.
Build systems, not one-offs
Design systems, reusable components, and typed contracts are the deliverable — because the next feature is cheaper and the next engineer is faster because of them.
How I think about architecture
Build past the ticket — a prototype worth building is worth building into a product
↗ Email PlatformAccessibility is verified, not claimed — if it wasn't audited, it isn't accessible
↗ Enterprise Product EngineeringOwn the whole feature — the API, the AI pipeline, and the accessible interface are one deliverable, not three handoffs
↗ SmartTalk AIShip the AI the way you'd ship any product feature — keys protected, responses streaming, experience responsive
↗ SmartTalk AITechnical domains
Product Frontend & Design Systems
Ships production-grade, responsive product UI in React and TypeScript — component architecture, design systems, and reusable libraries that keep the experience consistent and the team fast, with SSR and dynamic routing where it earns its keep.
Accessibility & Performance Engineering
Treats accessibility and speed as requirements, not polish — WCAG-compliant interfaces verified by audit, and data fetching and rendering optimized against real performance budgets.
AI Application Engineering
Builds AI-native product features end to end — retrieval-augmented generation over documents, streaming LLM responses, and the secure server-side plumbing that makes them production-safe.
Full-Stack Foundation
Owns a feature across the stack — API contracts, real-time transport, and a data layer chosen for the job — so the interface she builds is backed by services she understands end to end.
Core strengths
- Ship both halves of a modern app — the product-grade React experience and the AI-native features behind it, from one engineer
- Work like a product engineer, not a ticket-taker — take a prototype to a funded, production platform, with the product judgment to know it's worth it
- Treat accessibility as something to verify, not claim — audited with axe-core and Lighthouse to real WCAG compliance
- Build systems that compound — design systems, reusable component libraries, and typed contracts that make the next feature and the next engineer faster
- Bring the AI application skills companies are hiring for right now — RAG, streaming LLMs, and real-time systems, shipped end to end
Operating constraints I hold myself to
Certifications & education
Certifications
Education
M.S. Computer Science — Georgia State University, Atlanta, GA (2016) · GPA 3.71
Now you know how I think.
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