About

Pavithran S is a self-taught passionate developer with a strong interest in machine learning and its application in computer vision. With a current designation of Machine Learning Engineer at pixuate, Pavithran has gained 2.86 years of relevant experience in the industry. Pavithran has worked on a number of projects including a facial recognition app using MTCNN as detector and recognizer, which was deployed on a server using ZMQ. He also worked on a parking slot occupancy project using YOLO as detector and deployed it in JetsonNano using MQTT as a communication medium. Pavithran also developed and deployed cattle facial recognition using ZMQ. He has designed, developed, and deployed deep learning models using AI frameworks like TensorFlow, Keras, and PyTorch. Pavithran has also worked on developing a training tool to partially automate TensorFlow trainings with Neptune for logging, and an Excel tool to compare client data and AI system-generated data using Pandas and XlsxWriter. Pavithran has a Bachelor of Engineering degree in Electrical, Electronics and Communications Engineering from Sona College of Technology, completed in 2020. He has a strong tech stack including ML, Software Engineering, AI, Deep Learning, Keras, Computer Vision, TensorFlow, Search, OpenCV, and Edge Detection. Pavithran is continuously searching for challenging opportunities to learn and grow, challenge himself to solve real-world problems faced in the industry and day-to-day life. He is an agile worker with knowledge in tools like JIRA, Bitbucket, and GIT. In his past roles as a Machine Learning Engineer at Pixuate and an AI Developer at Smartbridge, Pavithran worked on a blurriness detection project using OpenCV edge detection and a speed detection project implemented using YOLO pretrained detector and OpenCV lightweight tracker. Pavithran has also conducted many workshops about IBM's Watson Studio.