An accomplished AI Engineer with a Bachelor's degree in Control and Automation Engineering, possessing significant expertise in robotics, computer vision, and deep learning. Proficient in a diverse range of technologies including Python, OpenCV, PyTorch, Linux, and C++, with substantial experience in mobile robotics and Agile methodologies. Demonstrated effectiveness in training transformer models for advanced applications in vegetation interference detection and developing innovative solutions for traffic violation detection, utilizing state-of-the-art techniques such as YOLOv8 and Docker-based microservices. Previous roles involved the design and implementation of face detection APIs and machine learning models for mobile applications, evidencing strong capabilities in both backend and mobile development environments.
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