Job Description

KEY RESPONSIBILTIES:

• Lead end-to-end computer vision initiatives, from defining problem statements and preparing datasets to developing, training, and deploying advanced vision models.
• Design, implement, and optimize algorithms for video-based object detection, tracking, action recognition, and real-time analytics across large-scale video streams.
• Develop robust, high-performance image and video processing pipelines using deep learning frameworks and GPU-accelerated technologies.
• Collaborate with engineering teams to integrate computer vision models into production systems, ensuring scalability, reliability, and low-latency performance.
• Guide and mentor junior engineers and researchers, providing technical leadership, code reviews, and support to strengthen overall team capability.
• Work closely with product, data, and solution teams to understand requirements, translate them into technical implementations, and deliver actionable vision-based insights.
• Implement best practices in model evaluation, versioning, optimization, and monitoring to ensure consistent model performance post-deployment.
• Stay current with emerging research in computer vision, video analytics, and deep learning, incorporating state-of-the-art techniques into ongoing projects.
• Communicate complex technical concepts and analytical results clearly to both technical and non-technical stakeholders.

QUALIFICATIONS, SKILLS AND EXPERIENCE:

• 5 plus years of experience in computer vision, deep learning, or video analytics with a proven record of production-grade deployments.
• Strong expertise with modern vision architectures (YOLO, Faster/Mask R-CNN, Vision Transformers) and deep learning frameworks such as TensorFlow or PyTorch.
• Hands-on experience with real-time video pipelines, streaming architectures, and edge AI deployments (e.g., NVIDIA Jetson).
• Practical experience in industrial vision systems such as machine state detection, anomaly identification, production line monitoring, or equipment performance analytics.
• Demonstrated ability to lead technical projects and mentor junior talent, fostering a culture of excellence and innovation.
• Excellent communication and presentation skills, capable of translating complex technical outputs into actionable insights.
• Strong problem-solving mindset and the ability to architect scalable end-to-end solutions.
• Experience working with distributed teams and offshore–onshore delivery models.
• Strong organizational and time management abilities with the capacity to handle multiple projects simultaneously.