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Mid-level experience range and Noida metro increase competition, but niche CV requirements moderate applicant density.
Specialized computer vision and video deployment skills limit cross-industry transferability.
Explicit 3–6 years requirement plus mandatory CV, deployment and optimization toolchain increases filtering stringency.
Own end-to-end development of computer vision models for real-time video analytics on multi-camera surveillance systems, including dataset curation, model training, and production deployment.
Build and optimize real-time inference pipelines for live camera streams using technologies like GStreamer, FFmpeg, DeepStream, and optimize models for edge/GPU using quantization, pruning, ONNX, TensorRT, OpenVINO/NPU toolchains.
Integrate video analytics outputs via APIs and metadata standards into NVR/VMS, dashboards, and third-party platforms for scalable, production-grade surveillance solutions.
3–6 years of hands-on experience in computer vision or deep learning with at least 2 years in production video analytics or surveillance.
B.Tech or M.Tech degree in Computer Science, Electronics, or related field.
Strong proficiency in Python and PyTorch or TensorFlow; experience with OpenCV and NumPy; comfortable with C++ for performance-critical code.
Experience with ONNX, TensorRT, DeepStream/GStreamer, Docker, Linux, and knowledge of RTSP/ONVIF, H.264/H.265 codecs, and camera imaging fundamentals.
Experienced in designing and deploying computer vision models specifically for surveillance use-cases like object detection, multi-object tracking, OCR/ANPR, and face recognition.
Able to handle end-to-end model lifecycle including annotation guidelines, dataset versioning, augmentation, retraining from field data, and optimization for edge devices.
Comfortable working in production-focused AI environments, collaborating cross-functionally with product, backend, deployment, and QA teams for scalable multi-camera site deployments.