Senior Computer Vision And Edge AI Support Engineer

7–10 years of overall IT experience with significant experience in AI/ML, Computer Vision, or application support. Strong knowledge of Computer Vision, Edge AI, image processing, and video analytics. Hands-on experience with Python and/or C++. Experience with OpenCV and at least one ML framework such as PyTorch or TensorFlow. Understanding of object detection, classification, segmentation, and image-processing pipelines. Experience deploying or supporting AI models on edge or embedded platforms. Strong Linux troubleshooting, log analysis, debugging, and performance-analysis skills. Experience in production support, incident management, root-cause analysis, and problem management. Understanding of REST APIs, containerization, networking, and cloud or edge connectivity. Strong communication, analytical, and stakeholder-management skills.

Provide production and application support for Computer Vision and Edge AI solutions. Monitor AI applications, inference services, edge devices, system health, logs, alerts, and performance metrics. Troubleshoot issues related to image-processing pipelines, video streams, AI models, cameras, edge devices, and system integrations. Perform root-cause analysis and drive timely resolution of production incidents. Support deployment and maintenance of Computer Vision models on edge platforms. Optimize AI inference for latency, throughput, memory utilization, and resource-constrained devices. Diagnose model, application, hardware-software integration, network, and environment-related issues. Work with Python, C++, OpenCV, PyTorch, TensorFlow, ONNX, or comparable Computer Vision technologies. Support object detection, image classification, segmentation, anomaly detection, and video-analytics solutions. Coordinate with AI/ML engineers, data scientists, platform teams, infrastructure teams, and business stakeholders. Document incidents, resolutions, known errors, operating procedures, and troubleshooting guides. Participate in incident, problem, change, release, and service-management activities. Identify opportunities to automate monitoring, support, deployment, and operational processes.