AI Computer Vision Engineer (Senior) — Vehicle Recognition

What you'll do

• 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, 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 and any junior engineers who join later. Review code and model evaluations.
• Stay current on practical CV research; bring back what's worth integrating, reject what isn't.
• What you'll bring
• 5+ years of production computer-vision work, including at least one role with senior or technical-lead responsibility.
• Deep C++ (C++17 or newer) and Python. Comfortable in both Linux and Windows build environments.
• Strong OpenVINO experience or equivalent (TensorRT, ONNX Runtime). You have personally optimised a model below an SLA you owned.
• ANPR / OCR / vehicle-recognition or comparable safety-critical CV background - ideally on edge hardware.
• Demonstrated ability to design model evaluation pipelines and ship measurable improvements over baselines.
• Linux command-line fluency.
• English fluency - written and spoken.
• Nice to have
• Direct GCC license-plate experience.
• Experience working with classified or restricted-access datasets and the operational disciplines that go with them.
• Experience leading 1-3 person AI teams.
• C++ inference on Intel platforms (OpenVINO model optimisation, INT8 quantisation, multi-threaded inference).
• Windows desktop application packaging.
• Experience with M1 / Apple Silicon for future portability.
• Arabic - reading useful for GCC plate work.

Location & work authorization