JungChan LEE
ML SYSTEMS ENGINEER · PRODUCTION PIPELINES
Self-taught ML engineer. Build production Python pipelines for LoRA fine-tuning, parameter optimization, and identity evaluation. Solo lead at The Blue Studio AI Lab. Sole author of the AZIT LoRA pipeline — nine Python scripts, training on RTX 5090, evaluated against a HuggingFace baseline.
Working environments
Two captures from the AZIT pipeline build — the dataset preparation view in AI-Toolkit, and the inference graph in ComfyUI. Full step-by-step trace in VISUAL WORKFLOW ↗.
AI-Toolkit dataset view — multi-view subject portraits prepared for LoRA fine-tuning
ComfyUI inference workflow — LoRA load, dual CLIP encode, KSampler, FaceDetailer (Impact Pack)
B.A. in Digital Content (2025), self-taught ML engineering through one production project at depth. I do not have years of web-scale backend experience; I do have evidence I can scope, build, ship, and measure a non-trivial ML pipeline solo, with documentation and quantitative outcomes.
I prefer to underclaim and let the artifacts — nine scripts, a benchmark, a structured sweep, a visual workflow trace — do the talking.