I am an experienced AI software engineer with five years of expertise in building and optimizing deep learning platforms. My work spans data pipelines, including preprocessing, augmentation, and hyperparameter tuning for training, as well as designing efficient distributed inference workflows using multi-processing/threading/GPU. I’ve also explored advanced techniques like distributed K-fold, model recompilation, model intermediate node extraction, and layer freezing.Currently, I am pursuing a part-time master’s degree in AI to deepen my technical expertise and stay at the cutting edge of the field. My career has included contributions to Deep Learning projects and hackathons, where I’ve worked on solutions like an LLM-enabled legal document review platform and a real-time table-tennis game tracking with Deep Learning. Additionally, I have mentored interns, helping them grow and contribute to AI projects effectively.Currently, I am pursuing a part-time master’s degree in AI to deepen my technical expertise and stay at the cutting edge of the field. My career has included contributions to deep learning projects focused on advanced topics such as LLMs, RAGs, and CNNs/VAEs. I’ve also participated in hackathons, working on solutions like an LLM-enabled legal document review and real-time table-tennis game tracking using DL. Additionally, I have mentored interns, guiding them in projects ranging from exploration of potential AI technologies to developing AI distributed testing frameworks, helping them grow and effectively contribute to AI initiatives.I’m passionate about leveraging my expertise in AI and software engineering to tackle complex problems and drive meaningful impact. My focus is on optimizing efficiency and building robust, scalable solutions that push the boundaries of what technology can achieve.