Senior software engineer → AI systems engineer

I build AI systems that can explain their quality, performance, and failure modes.

Current focusProduction AI EngineeringPhase 1 · Applied deep learning

01Quality before optimization

Evaluation sets, baselines, and error analysis precede model changes.

02Production evidence

Reliability, observability, rollout, and rollback are part of the model lifecycle.

03Hardware-aware path

Phase 2 advances into CUDA, TensorRT, Jetson, and measurable Edge AI.

P-01Active case study

Computer vision · Evaluation · Production inference

Vision Quality Baseline

Building a reproducible image-classification baseline with explicit data, quality, runtime, and limitation evidence.

Macro F1 target≥ 88%CPU P95 target≤ 80msEvidence3 / 6