Video Analytics EngineerWe 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 ResponsibilitiesDesign, 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 QualificationsBachelor'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 QualificationsExperience 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 SkillsPythonC++ (preferred)OpenCVPyTorchTensorFlowYOLODetectron2MMDetectionDeepSORTByteTrackOC-SORTStrongSORTNVIDIA DeepStream SDKGStreamerFFmpegTensorRTONNX RuntimeOpenVINODockerKubernetesGitLinuxREST APIsSQLAWS / Azure / Google Cloud PlatformSoft SkillsStrong analytical and problem-solving abilitiesExcellent communication and collaboration skillsAttention to detail and engineering disciplineAbility to work in Agile and cross-functional teamsInnovation and continuous learning mindsetStrong debugging and troubleshooting capabilitiesNice to HaveExperience with smart surveillance, intelligent transportation systems, sports analytics, or industrial automationKnowledge of edge AI optimization, model quantization, and real-time inferenceExperience with multimodal AI combining video, audio, and textFamiliarity with privacy-preserving AI, Responsible AI, and video data governanceContributions to open-source computer vision or video analytics projectsAI, cloud, or computer vision certificationsKey Performance Indicators (KPIs)Object detection, tracking, and event recognition accuracyVideo processing latency and throughputPrecision, recall, F1-score, and mAP for deployed modelsSystem uptime and production reliabilityReduction in false positives and false negativesSuccessful deployment of scalable video analytics solutionsResource utilization and edge inference efficiencyTimely delivery of new analytics features and performance improvementsLocationHybrid / Remote / On-site (as applicable)Employment TypeFull-time