We are seeking a highly experienced Lead Computer Vision to spearhead the research, architecture, and productization of AI-based video analytics and biometric solutions.
This role demands deep expertise in Computer Vision, Deep Learning, Edge AI, Linux, and GStreamer frameworks for developing intelligent camera-based systems used in identity, security, and smart IoT applications.
The incumbent will lead the design and deployment of real-time vision algorithms on embedded platforms and mobile devices, transforming innovation into scalable commercial products.
Key Responsibilities
- Lead end-to-end development of computer vision and deep learning solutions for edge devices such as smart cameras, biometric terminals, and IoT vision systems.
- Architect and optimize real-time video pipelines using GStreamer on Linux-based embedded platforms.
- Design and implement DL / CV models (object detection, segmentation, tracking, gesture recognition, facial analytics, etc.) using TensorFlow, PyTorch, OpenCV, DLIB, Mediapipe, and TFLite.
- Drive AI model lifecycle management dataset curation, training, validation, quantization, and deployment on embedded / edge hardware.
- Collaborate with cross-functional teams for hardware integration, firmware communication, and cloud / mobile interfacing.
- Ensure robust video streaming, processing, and performance optimization for real-time applications using GStreamer and multimedia APIs.
- Lead a team of engineers, promoting R&D excellence, mentoring talent, and generating IP / patents.
- Collaborate with product and design teams to transform research ideas into production-grade solutions.
- Stay ahead of emerging trends in Edge AI, CV frameworks, and embedded vision architectures.
Required Skills & Technical Expertise
Must Have :
Strong expertise in Linux-based development and GStreamer multimedia framework (pipeline design, custom plugin development, optimization for video capture and streaming).Proficiency in C / C++ and Python programming for performance-critical applications.Hands-on experience with Computer Vision and Deep Learning frameworks TensorFlow, PyTorch, OpenCV, DLIB, Mediapipe, and TFLite.Experience in Edge AI inference optimization (quantization, pruning, and deployment on NPUs / DSPs / ARM platforms).Solid understanding of image and video processing techniques, including object detection, recognition, tracking, segmentation, and gesture recognition.Exposure to Android / iOS camera systems, embedded Linux, and real-time video analytics.Experience in software architecture design for camera-based AI products (Edge + Cloud integration).Excellent problem-solving, debugging, and system-level understanding of video processing pipelines.Good to Have :
Exposure to biometric systems (face, iris, fingerprint).Experience with multimedia middleware (V4L2, FFmpeg).Familiarity with 3D vision, SLAM, or sensor fusion.Understanding of IoT and secure data streaming architectures.Qualification & Experience
B.Tech / M.Tech / Ph.D. in Computer Science, Electronics, or related fieldExperience in Computer Vision, Deep Learning, Linux, and GStreamer-based systemsProven track record of developing and deploying AI-driven camera products or embedded vision systems