//assets.cakeresume.com/assets/contentarea/contentarea-2d64eca8dae2a1ffb31e0fb1df887bbad072c7c19734d7d30fa14884bbcc299a.css

Google Data Analytics Certificate

Coursera

ML/MLOps engineer with over 4 years of practical experience in developing and optimizing models related to Computer Vision (CV) and Natural Language Processing (NLP). 

Proficient in building and utilizing existing MLOps platform to address issues related to data, models, parameters, code version management, ML pipeline management, and model performance monitoring.

• Researched open dialogue systems in NLP to enhance multi-turn conversation models (Transformer-based) and evaluation methods.

National Central University

• Established an intelligent IoT cane for visually impaired individuals and participating in the ACM SIGCHI Student Design Competition.
• Developed a web-based appointment booking system for the Office of Student Affairs.

National Yunlin University of Science and Technology

Machine Learning Engineer

Chun-Chieh Huang

Taiwan Semiconductor Manufacturing Company Limited

Senior Machine Learning Engineer

• Served as a technical PM, clarifying user needs and overseeing development schedule and progress.
• Developed a CNN-based algorithm for classifying abnormal discharge events in power components , achieving a 75% F1 score. Utilized Grad-CAM for model validation to align features with expert insights.
• Developed FastAPI backend APIs to integrate ML predictions, enabling user feedback via frontend, enhancing model accuracy through retraining.
• Utilizing in-house TSMC MLOps platforms to achieve CI/CD, and to accomplish data, models, parameters, code version management, and model monitoring & retraining.

台灣台中市

• Developed wafer defect inspection algorithms, integrating rule-based and deep learning models, achieving an 80% F1 score.
• Created a YOLOv5-based (Object Detection) safety monitoring system for construction sites, assessing equipment integrity and identifying unsafe behaviors, with a 95% F1 score in POC tests.
• Overcame limited data challenges in early experimental stages using few-shot and active learning, enhancing project progression.

分享