Set the technical bar across our vehicle‑detection and license‑plate‑recognition stack.

• Govern model architecture choices, dataset strategy, evaluation pipelines, and deployment patterns.
• Design, train, optimise, and deploy CV models for vehicle detection, classification, type, colour, brand, model, and GCC license‑plate recognition.
• Convert and optimise models (YOLO family and others) for inference on Intel CPUs using OpenVINO; profile and reduce latency on edge hardware until you hit the SLA you own.
• Drive performance work on inference latency, model footprint, and CPU resource use for fleet‑scale deployment.
• Govern the labelling guideline with the AI labelling operator, prioritise edge cases, audit dataset quality, and sign off on dataset releases.
• Partner with the full‑stack and web teams to expose model outputs through stable APIs that survive model updates.
• Mentor the AI labelling operator ...