Architecture with context
The right solution accounts for the product, team, operating model, and expected rate of change—not only the code.
About
I translate complex AI and data systems into intuitive products.
For more than a decade, I have built high-performance software across Google, Globant, Canva, and Restb.ai, from telemetry and enterprise modernization to real-time collaboration and AI-powered search.
My strongest work begins with an ambiguous, high-leverage problem: a product slowing down under real data, an AI capability users cannot confidently control, or a frontend architecture that makes every feature harder to ship.
I design production AI experiences spanning assistants, semantic search, RAG, embeddings, vector retrieval, streaming responses, and human-in-the-loop workflows. I pair that work with scalable React platforms, design systems, state architecture, and measurable browser performance.
I own outcomes from architecture design through production operations: engineering standards, testing, observability, releases, troubleshooting, mentoring, and alignment across product, design, backend, and machine learning teams.
Engineering principles
Performance as product quality
Latency, stability, accessibility, and clarity are part of the user experience and deserve measurable goals.
Multiplying the team
Strong technical direction creates shared language, reduces repeated decisions, and helps other engineers do their best work.