Katomaran Technologies
Website:
katomaran.com
Company:
https://www.linkedin.com/company/katomaran
Seniority: Mid-Senior level
Industries: Software Development
Job details:
Job Title: Senior Video Analytics (VA) Developer
Experience: 5–8 Years
Location: Coimbatore (On-site)
Employment Type: Full-time
Company: Katomaran Technologies
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 & Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Electronics, or a related field.
- 5–8 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.
Click on Apply to know more.
