Job Title: Video Analytics EngineerJob Summary We are seeking a Video Analytics Engineer to design, develop, and deploy AI-powered video analytics solutions for real-time and offline video processing. The ideal candidate will have expertise in computer vision, deep learning, image processing, video analytics, and edge AI. This role involves building intelligent video analysis systems for object detection, tracking, activity recognition, anomaly detection, and event analytics while ensuring scalable, low-latency, and production-ready deployments.
Key Responsibilities - Design, develop, and optimize AI-powered video analytics applications for real-time and batch video processing.
- Develop computer vision models for object detection, object tracking, instance segmentation, activity recognition, event detection, and anomaly detection.
- Build video processing pipelines for surveillance, industrial inspection, retail analytics, traffic monitoring, healthcare, sports analytics, and smart city applications.
- Develop multi-camera analytics and cross-camera object re-identification solutions.
- Optimize AI inference for low-latency, high-throughput video streaming environments.
- Implement video preprocessing techniques, including frame extraction, stabilization, enhancement, compression, and synchronization.
- Integrate video analytics solutions with CCTV systems, IP cameras, edge devices, cloud platforms, and enterprise applications.
- Develop APIs and microservices for video analytics deployment and integration.
- Deploy and manage AI models using MLOps and cloud-native practices.
- Monitor production model performance and continuously improve detection accuracy and operational efficiency.
- Collaborate with AI Engineers, Data Scientists, Software Engineers, and Product teams to deliver production-ready solutions.
- Document solution architecture, algorithms, testing results, and deployment procedures.
Required Qualifications - Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Computer Vision, Electronics, Electrical Engineering, or a related field.
- 3+ years of experience in computer vision, video analytics, AI engineering, or machine learning.
- Strong programming skills in Python; C++ is an advantage.
- Experience with OpenCV and deep learning frameworks such as PyTorch or TensorFlow.
- Knowledge of video processing concepts, codecs, and streaming technologies.
- Experience developing AI models for object detection, tracking, and activity recognition.
- Familiarity with Linux, Git, Docker, and REST APIs.
- Understanding of machine learning model evaluation and optimization techniques.
Preferred Qualifications - Experience with object detection frameworks such as YOLO, Detectron2, MMDetection, or Faster R-CNN.
- Experience with multi-object tracking algorithms such as DeepSORT, ByteTrack, OC-SORT, or StrongSORT.
- Knowledge of video action recognition, pose estimation, and behavior analysis.
- Experience with NVIDIA DeepStream SDK, GStreamer, FFmpeg, TensorRT, OpenVINO, or ONNX Runtime.
- Experience deploying AI applications on NVIDIA Jetson or other edge AI devices.
- Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
- Experience with Kubernetes, CI/CD pipelines, and MLOps practices.
- Knowledge of Generative AI for video summarization, captioning, or synthetic video generation.
Technical Skills - Python
- C++ (preferred)
- OpenCV
- PyTorch
- TensorFlow
- YOLO
- Detectron2
- MMDetection
- DeepSORT
- ByteTrack
- OC-SORT
- StrongSORT
- NVIDIA DeepStream SDK
- GStreamer
- FFmpeg
- TensorRT
- ONNX Runtime
- OpenVINO
- Docker
- Kubernetes
- Git
- Linux
- REST APIs
- SQL
- AWS / Azure / Google Cloud Platform
Soft Skills - Strong analytical and problem-solving abilities
- Excellent communication and collaboration skills
- Attention to detail and engineering discipline
- Ability to work in Agile and cross-functional teams
- Innovation and continuous learning mindset
- Strong debugging and troubleshooting capabilities
Nice to Have - Experience with smart surveillance, intelligent transportation systems, sports analytics, or industrial automation
- Knowledge of edge AI optimization, model quantization, and real-time inference
- Experience with multimodal AI combining video, audio, and text
- Familiarity with privacy-preserving AI, Responsible AI, and video data governance
- Contributions to open-source computer vision or video analytics projects
- AI, cloud, or computer vision certifications
Key Performance Indicators (KPIs) - Object detection, tracking, and event recognition accuracy
- Video processing latency and throughput
- Precision, recall, F1-score, and mAP for deployed models
- System uptime and production reliability
- Reduction in false positives and false negatives
- Successful deployment of scalable video analytics solutions
- Resource utilization and edge inference efficiency
- Timely delivery of new analytics features and performance improvements
Location Hybrid / Remote / On-site (as applicable)
Employment Type Full-time
