JUNGCHAN LEE
ML SYSTEMS ENGINEER
Self-taught ML engineer. Build production Python pipelines for LoRA fine-tuning, parameter optimization, and identity evaluation. Sole author of the AZIT LoRA pipeline (independent R&D for K-pop digital human) — nine Python scripts, training on RTX 5090, evaluated against a commercial baseline (Higgsfield Soul 2.0). Most recently AI Lab Lead at The Blue Studio, applying the same techniques on real actor photography.
KEY METRIC · KEY RESULT
0.6050 ArcFace · vs 0.5090 baseline
AZIT LoRA on FLUX.2 Klein 9B ·
+18.9% OVER BASELINE
MOST RECENT ROLE
AI Lab Lead
The Blue Studio — sole engineer
- PERIOD
- March 2026 — April 2026
- SCOPE
- LoRA training · inference · pipeline build
- STACK
- Python · ComfyUI · AI-Toolkit · ArcFace
- STATUS
- Open to roles
WORKING ENVIRONMENTS
Tools that ship every dependency
We capture how the AZIT pipeline runs — the dataset preparation step in AI-Toolkit, and the inference graph in ComfyUI. Full step-by-step trace in VISUAL WORKFLOW ↗.
HONEST FRAME
B.A. in Digital Content (DCDC, 2025), self-taught ML engineering through
one production project at depth. I do not have years of ML-board-backed
experience; I do have evidence I can scope, build, ship, and measure a
non-trivial ML pipeline solo — nine scripts, a benchmark, a
structured sweep, a visual workflow trace — doing the talking
instead of credentials.