Computer Vision Lead Engineer

Responsibilities-Define the architecture and roadmap for real-time, multi-camera airport video analytics. Lead detection, segmentation, tracking, OCR, action recognition, and operational-event solutions. Scale inference across 100+ streams using NVIDIA, ONNX, DeepStream, Triton, and edge technologies. Establish accuracy, latency, throughput, availability, false-alarm, and degraded-mode criteria. Own model monitoring, drift detection, versioning, reproducibility, rollback, and production readiness. Lead recruitment, mentoring, engineering standards, stakeholder alignment, and technical delivery. Requirements- Master’s or PhD in Computer Science, AI, Electrical Engineering, or related discipline. 7+ years in computer vision or deep learning, including 2+ years in technical leadership. Strong Python, TensorFlow, PyTorch, Keras, OpenCV, and ONNX experience. Proven production experience with scalable, real-time video analytics. Strong cloud, edge, GPU, Docker, Kubernetes, data-pipeline, and MLOps knowledge. NVIDIA stack, multi-camera, aviation, industrial, or mission-critical experience is preferred. Soft Skills Hands-on technical leadership and sound architectural judgement. Strong research-to-production mindset. Honest communication of AI uncertainty, limitations, and operational risks. Focus on operator trust and false-alarm management. Strong mentoring, prioritisation, and stakeholder-management capability. Accountability for production outcomes, not only model accuracy.