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Job Title: Senior Video Analytics (VA) Developer Experience: 58 Years Location: Coimbatore (On-site) Employment Type: Full-time About the Role Katomaran Technologies is looking for an experienced Senior Video Analytics Developer to design, develop, and deploy AI-powered video analytics solutions. The ideal candidate should have strong expertise in computer vision, deep learning, and real-time video processing, with experience in developing scalable video analytics applications for surveillance, industrial automation, smart cities, and security solutions. Key Responsibilities Design, develop, and maintain production-grade AI-powered video analytics applications for real-time video processing. Architect and build scalable end-to-end video analytics pipelines using GStreamer, DeepStream, OpenCV, FFmpeg, and RTSP/HTTP streaming protocols. Develop and optimize computer vision solutions for object detection, multi-object tracking, image classification, semantic/instance segmentation, face analytics, license plate recognition, and activity recognition. Optimize AI inference performance using NVIDIA TensorRT, CUDA, ONNX Runtime, DeepStream SDK, and GPU acceleration techniques to maximize throughput and minimize latency. Deploy, configure, and maintain video analytics applications on NVIDIA Jetson platforms, edge AI devices, and GPU-based Linux servers. Design and deploy large-scale video analytics solutions supporting hundreds to thousands of IP cameras in production environments with high availability and fault tolerance. Integrate AI applications with IP cameras, CCTV systems, VMS platforms, cloud services, databases, message brokers, REST APIs, and backend microservices. Develop robust streaming pipelines capable of handling multiple concurrent video streams with efficient resource utilization, batching, and hardware acceleration. Profile and optimize CPU, GPU, memory, and network performance to improve system scalability, reliability, and real-time processing capabilities. Troubleshoot complex production issues related to video streaming, DeepStream pipelines, GStreamer plugins, AI inference, hardware acceleration, and distributed deployments. Collaborate closely with AI/ML Engineers, Backend Developers, Embedded Engineers, DevOps, and QA teams throughout the product lifecycle. Mentor junior engineers through technical guidance, architecture reviews, code reviews, debugging support, and engineering best practices. Contribute to software architecture decisions, technical documentation, coding standards, CI/CD practices, and system design improvements. Evaluate and adopt emerging technologies in Computer Vision, Edge AI, GPU computing, and Video Analytics to continuously improve the product. Ensure production readiness by implementing monitoring, logging, automated recovery mechanisms, health checks, and performance benchmarking. Required Skills and Qualififcations Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Electronics, or a related field. 58 years of experience in Computer Vision, Video Analytics, or AI application development. Strong proficiency in Python and/or C++. Hands-on experience with OpenCV, PyTorch, TensorFlow, or similar deep learning frameworks. Experience working with NVIDIA DeepStream, GStreamer, and FFmpeg. Good understanding of computer vision and deep learning concepts. Experience with real-time video streaming protocols such as RTSP, RTP, and ONVIF. Knowledge of GPU acceleration technologies including CUDA, TensorRT, and NVIDIA Jetson platforms. Experience with Linux, Docker, Git, and REST API integration. Familiarity with databases, message brokers (Kafka, RabbitMQ, MQTT, or Redis), and cloud or edge deployments is an advantage. Strong analytical, debugging, and problem-solving skills. Good communication skills and the ability to work effectively in a collaborative team environment. Nice to have Experience deploying AI applications on edge devices and embedded vision systems. Familiarity with Kubernetes, cloud platforms (AWS, Azure, or GCP), and messaging technologies such as Kafka, RabbitMQ, or MQTT. Knowledge of MLOps practices, AI model optimization, and model lifecycle management. Experience in Video Analytics domains such as Smart Surveillance, Traffic Analytics, Industrial Safety, Retail Analytics, Smart Cities, or Intelligent Transportation Systems (ITS). Experience with NVIDIA Triton Inference Server or other AI model serving frameworks. Understanding of CI/CD pipelines and DevOps practices for AI application deployment. Ability to lead technical initiatives, mentor team members, and drive engineering best practices. .
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