About

Self-taught ML systems engineer. I came to engineering from digital-content production and converged into ML when I needed identity-preserving image generation for an actor-driven studio project that had no off-the-shelf solution good enough.

The way I work: read the field bibles (FPHam, djdante, CivitAI, HuggingFace community), turn the synthesis into a written principle document, implement the principle as code, then measure against a public baseline. The AZIT LoRA pipeline is the first project I’ve taken from blank repository to benchmark-beating result solo, and it’s the one I keep going back to when I want to remember how to ship something.

On the audio side, I run K-Pop production end-to-end (separate face at /audio). The two practices reinforce each other: audio taught me to A/B against commercial references on every decision, which is the same instinct that makes ML evaluation honest.

I am at the start of an engineering career, not in the middle of one. B.A. in Digital Content (2025), one production ML project at depth, no years of web-scale backend experience.

What I do have: evidence I can scope, build, ship, document, and measure a non-trivial ML pipeline solo. Nine scripts, a benchmark, a structured sweep, and a written reasoning trail for each decision. I prefer to underclaim and let the artifacts do the talking.

Best fit: mid-level individual contributor, working alongside senior engineers on AI-native product work, with room to ramp on web stack (TypeScript, React, Postgres) where projects need it.