Website:
etiot.in
Job details:
Company: ETIOT
Experience: 5+ Years
Location: Kolkata | Work from Office
Department: Engineering / Product Development
Role Overview
ETIOT is looking for a hands-on Video Analytics (VA) & Video Management System (VMS) Engineer with strong software development, video integration, and AI-powered computer vision experience. The candidate will work on integrating IP Cameras/CCTV systems, video streaming protocols, VMS platforms, and AI/ML-based analytics to build scalable, real-time video intelligence solutions across edge, cloud, and hybrid environments.
The role requires practical expertise in video processing, system integration, performance optimization, and deploying production-ready video surveillance and analytics applications.
Key Responsibilities
- Develop, integrate, and deploy Video Management Systems (VMS) and Video Analytics (VA) solutions supporting IP Cameras/CCTV, live video streaming, recording, playback, storage, and retention.
- Implement and troubleshoot ONVIF, RTSP/RTP, and related video streaming protocols, including interoperability across camera brands, codecs such as H.264/H.265, and third-party VMS platforms.
- Build real-time video processing and computer vision pipelines for object detection, object tracking, people and vehicle detection, ANPR/LPR, face detection/recognition, perimeter intrusion detection, loitering detection, PPE detection, crowd/behavior analytics, and video event detection.
- Develop and optimize AI/ML and Deep Learning models using OpenCV, PyTorch, TensorFlow, or similar frameworks.
- Work with C++ (strongly preferred) and Python for video processing, analytics integration, system development, and debugging.
- Leverage FFmpeg/GStreamer, NVIDIA CUDA, and TensorRT for video pipeline development, GPU acceleration, and inference optimization.
- Design and integrate REST APIs, WebSocket interfaces, microservices, and API-first components for seamless communication between VMS, analytics engines, and external applications.
- Deploy and maintain solutions on Linux-based systems using Docker/Kubernetes, with data storage and messaging components involving PostgreSQL, MongoDB, Redis, or equivalent technologies.
- Contribute to VMS Architecture, Video Analytics Architecture, System Design, and Product Architecture for distributed, multi-camera deployments.
- Support edge AI, cloud/hybrid video processing, GPU-based processing, and distributed video processing architectures.
- Build scalable, highly available, real-time video solutions with multi-camera architecture and, where applicable, multi-tenant SaaS capabilities.
- Conduct integration testing, performance benchmarking, troubleshooting, and production deployment across different hardware and software environments.
Required Technical Skills
- Video & VMS: Video Management System (VMS), Video Analytics (VA), IP Camera/CCTV integration, Video Streaming, Video Recording & Playback, Video Storage & Retention.
- Protocols & Codecs: ONVIF, RTSP/RTP, H.264/H.265.
- AI/ML & Computer Vision: Deep Learning, OpenCV, Object Detection, Object Tracking, ANPR/LPR, Face Detection/Recognition, People & Vehicle Detection, Perimeter Intrusion Detection, Crowd/Behavior Analytics, PPE Detection, Loitering Detection, and Video Event Detection.
- Programming & Video Processing: C++ (strongly preferred), Python, FFmpeg/GStreamer.
- AI Acceleration & Frameworks: NVIDIA CUDA, TensorRT, PyTorch, TensorFlow.
- Backend & Integration: REST APIs, WebSocket, Microservices.
- Infrastructure & Data: Linux, Docker/Kubernetes, PostgreSQL, MongoDB, Redis, or equivalent technologies.
Architecture & Engineering Competencies
- VMS Architecture and Video Analytics Architecture.
- Real-time Video Processing and Distributed Video Processing.
- Multi-camera architecture and scalable system design.
- High Availability and performance optimization.
- Edge AI, Cloud/Hybrid Architecture, and GPU-based Video Processing.
- Multi-tenant SaaS and API-first Architecture.
- System Design, Product Architecture, and end-to-end integration ownership.
Preferred Profile
- 5 years of relevant experience in video software engineering, VMS development, video analytics, computer vision, or video platform integration.
- Demonstrated hands-on experience integrating multiple IP camera models, VMS platforms, video streams, and AI analytics engines.
- Strong debugging and problem-solving skills across video protocols, networking, streaming, inference, and deployment environments.
- Experience taking solutions from proof of concept to production, including performance tuning and operational troubleshooting.
- Ability to collaborate across software, AI/ML, infrastructure, and product teams.
Ideal Candidate
A software-oriented engineer who understands how cameras, video protocols, VMS platforms, AI/ML models, and distributed systems work together. The candidate should be capable of building and integrating end-to-end video intelligence solutions, with a strong emphasis on practical implementation, reliable deployment, and scalable product engineering.
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