Operational discipline
Typed interfaces, observability, and failure modes matter as much as raw throughput.
About
I build AI systems, software platforms, and infrastructure with a bias for clarity.
I am a Senior AI/ML Engineer at Dais, where I lead machine-learning research and development across Rust-based context systems and durable agent platforms. My broader background spans applied ML, systems software, medical-data tooling, and Kubernetes infrastructure.
This site is the public version of that work. It is organized around case studies, technical writing, and smaller experiments that show how I approach platform design, distributed systems, WebAssembly, applied ML, and reproducible research.
How I work
I aim for systems that remain understandable after they are deployed.
Platform thinking
I prefer systems that make change safer: GitOps, automation, reproducible builds, and clear ownership boundaries.
Writing as engineering
Documentation and technical explanation are part of the product, especially when the work includes proofs, benchmarks, or tradeoffs.
Why rebuild the site
The previous site worked, but it was harder to extend than it needed to be.
This version is built as a more deliberate editorial system: clearer navigation, stronger project framing, better case-study layouts, and enough structure to support code, formulas, and interactive demos without breaking the page flow.