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.