About the RoleWe're seeking an experienced Computer Vision Engineer to develop an edge AI system for sports field usage monitoring and predictive maintenance. You'll build and deploy YOLOv8 models on NVIDIA Jetson devices to detect and track players on synthetic turf fields in real-time, generating actionable insights for maintenance crews and coaches.Key Responsibilities:Train, optimize, and deploy custom YOLOv8 object detection models for player trackingConfigure and manage Frigate on NVIDIA Jetson edge devices for sub-100ms inferenceDevelop zone-based tracking algorithms and wear pattern analysisBuild MQTT pipelines for streaming detection events to cloud infrastructureFine-tune models based on real-world field testing and edge deployment challengesRequirements:Strong experience in computer vision, including image processing, feature extraction, object detection, tracking, and classification techniquesProven experience with YOLO object detection models (v5, v8, or similar)Hands-on deployment of models on edge devices (preferably Jetson, embedded systems)Strong Python proficiency for model development and optimizationExperience with real-time video processing and performance optimizationProblem-solving mindset for production deployment challengesNice-to-Have:Frigate NVR or similar video analytics platform experienceNVIDIA Jetson device deploymentMQTT or IoT messaging protocolsModel quantization and edge inference optimization