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Let’s connect
Distributed systems, AI infrastructure, cloud architecture — or a problem that doesn’t fit any of those boxes. I’m open to internships, new grad roles, and good conversations.
Open to 2027 internships & new-grad roles
I build AI systems efficient enough to actually ship — agent runtimes, LLM inference pipelines, and enterprise automation that hold up under real cost, latency, and reliability constraints.
Selected work
Each one taught me something I could not have read my way to. Hover for the shape of it, open for the tradeoffs.
Background
Three years across AI systems, enterprise platforms, a startup, and a research lab. Open any one for the numbers and what actually shipped.
Decision making
Every stack choice is a trade. Here are some I made, what I gave up for them, and what I'd reconsider.
SSR + ISR for SEO, App Router for streaming RSCs, and Vercel-native deployment with zero config. Server components eliminate client-side data fetching waterfalls.
Remix (nested routing), Gatsby (static-only), Vite + React SPA
Heavier bundle and framework lock-in vs. full-stack capabilities and incremental static regeneration
Tools
Roughly the order a request falls through — from the language it is written in, down to where the bytes finally rest.
Depth of use varies — the honest breakdown lives on the skills page, graded from real activity rather than self-report.
On GitHub, right now
Activity
Pulled straight from GitHub — the actual commits, not a curated highlight reel.