Summary
Eungyu Jin is an AI Research Engineer based in Seoul with nine years of experience building and deploying production-grade machine learning systems across vision and time-series domains. He has a strong track record optimizing object detection and inference pipelines for satellite and fashion imagery, reducing latency and memory issues while enabling reliable production deployments on Kubernetes. His work spans Bayesian time-series models and automated HPO with Kubeflow, showing a blend of probabilistic modeling and scalable MLOps that improves decision-making in manufacturing and sensor-driven applications. Eungyu also builds end-to-end ML pipelines—ETL with Airflow, training and retraining processes, and deployment orchestration—prioritizing reproducibility and business impact. Though his GitHub notes front-end interests, his career reveals deep expertise in backend ML engineering and performance optimization, rooted in a physics background from Hanyang University.
9 years of coding experience
4 years of employment as a software developer
학사, 물리학, 학사, 물리학 at 한양대학교