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.