Manpreet Minhas is a Senior Deep Learning Research Engineer with eight years of experience building computer vision and NLP systems that move from research to real-world impact. He has led projects spanning anomaly detection, segmentation, object detection and tracking, and information retrieval, and has a demonstrated knack for compact, high-performing models (e.g., AnoNet’s 65k-parameter architecture achieving SOTA anomaly detection). Comfortable across PyTorch, TensorFlow, HuggingFace and production languages like Python and Rust, he also experiments with generative AI, LLMs/VLMs and vector databases to bridge research and applied products. His background combines a strong academic foundation (Master’s in Deep Learning and Computer Vision, University of Waterloo) with industry experience at Zefr and Fugro, and past work deploying automated redaction and road-crack detection systems. He publishes and communicates ideas (Towards Data Science author), indicating an ability to translate technical advances for broader audiences. Based in Toronto, he’s focused on practical, efficient ML solutions that scale beyond the lab.
8 years of coding experience
10 years of employment as a software developer
B. Tech, Electronics and Communication Engg., B. Tech, Electronics and Communication Engg. at National Institute of Technology Kurukshetra
M.A.Sc, Circuits and Systems, FPGA, M.A.Sc, Circuits and Systems, FPGA at University of Waterloo
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.