Overview

Years of engineering across machine learning, backend systems, and production software well before the current LLM wave.

Approach

Experience across Ruby, Java, Python, Haskell, and Julia informs a practical approach to modern AI work: understand the system, keep the architecture coherent, and optimize for reliability as well as speed.

Why it matters

Useful context for clients who want more than prompt experimentation and need someone with deep technical judgment across the stack.