Labeling was slow, inconsistent, and the bottleneck on every model we wanted to ship. I built the tooling and the guidelines that made it fast and repeatable, and put the annotator disagreements to work as a signal instead of noise.

Unified training and serving features behind one definition; the accuracy gap nobody could explain simply disappeared.

Everyone wanted 'AI in the product' and nobody had said what good would look like, so we were one demo away from shipping something confidently wrong. I set the evaluation first — the metric, the holdout, the bar to clear — so we could tell real progress from a nice-looking demo.

Built labeling tools and guidelines; throughput doubled and inter-annotator agreement rose enough to trust the data.

The models were good in a notebook and useless in production, where nothing was reproducible and every handoff lost something. I built the path from experiment to serving that actually held up — versioned data, a real evaluation gate, and deploys that could be rolled back without ceremony.

Instrumented live predictions for drift and degradation on 11 models; problems surfaced on a dashboard, not in support.