Technology Lead - Computer Vision

Qualification: B.Tech/B.E., BCA, B.Sc., M.Tech, MCA, or M.Sc. Role Overview We are looking for a Technology Lead Computer Vision to lead the design, development, and delivery of innovative computer vision solutions that transform images and video into actionable insights. You will work closely with engineers, data scientists, and product teams to define technical direction, develop scalable vision solutions, mentor engineers, and drive projects from prototype to production. Key Responsibilities Lead the end-to-end development of Computer Vision solutions using OpenCV and YOLO , from data understanding and experimentation to production deployment. Design and implement object detection pipelines , including preprocessing, model inference, and post-processing. Build, train, and fine-tune CNN/deep learning models using PyTorch . Conduct experiments, evaluate model performance, and maintain reproducible documentation. Drive optimization of vision models for speed, memory, latency, and throughput . Improve model robustness across real-world conditions such as lighting variations, occlusion, motion blur, and scale variations . Apply model optimization techniques such as pruning, quantization, and mixed-precision inference/training . Define technical standards for model development, coding, version control, and code reviews. Collaborate with product and engineering teams to translate business requirements into scalable technical solutions. Mentor engineers, guide technical troubleshooting, and resolve complex issues across data, training, and inference. Support the integration of computer vision models into production environments, including monitoring and continuous improvement. Participate in sprint planning and drive cross-functional delivery of computer vision initiatives. Required Skills Strong hands-on experience in Computer Vision and Deep Learning . Proficiency in Python . Strong experience with OpenCV and YOLO/Object Detection . Hands-on experience with PyTorch and CNN/deep learning models. Strong understanding of image processing, model training, inference, and post-processing . Experience optimizing models for production environments. Good understanding of model evaluation metrics and experimentation techniques. Experience working with real-world computer vision challenges. Preferred Skills Experience with dataset iteration, ablation studies, and metric-driven model selection . Experience with model pruning, quantization, and mixed-precision optimization . Experience deploying computer vision models into production. Exposure to cloud/platform engineering and production ML workflows. Strong experience in technical leadership, mentoring, sprint planning, and stakeholder management . Ideal Candidate The ideal candidate is a hands-on Computer Vision/AI/ML professional with 511 years of experience , strong expertise in OpenCV, YOLO, PyTorch, and Object Detection , and proven experience leading technical initiatives and mentoring engineering teams. .