Responsibilities
- 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.
- 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.
Preferred qualifications
- 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.
- Are you willing to work in the UAE time zone from 9 AM to 5 PM?
- Rate your C++ proficiency on a 1–5 scale, where 5 means you have shipped C++17+ to production and reviewed others' C++ code.
- Have you converted and deployed a YOLO-family model to edge hardware?
- What is your expected monthly base salary in AED? (Number)
- Do you currently live in, or are willing to relocate, to Abu Dhabi?
- computer-vision work: 5 years (Required)
- Arabic (Preferred)
About the company
Safe City Group is a UAE-based public-safety and smart-city technology operator. We build the computer-vision, video-management, and integration platforms that sit behind city-scale ANPR, traffic, and safety services across the GCC. Our AI team is working on vehicle detection and classification (type, colour, brand, model), GCC plate recognition, and real-time inference on edge hardware. The team is small, the work is hands-on, and what you ship is in production within weeks.
We are hiring two senior engineers onto this team. Both seats are senior — there is no junior or mid role behind this posting. You will be working alongside another senior engineer of comparable depth, and together you will set the technical bar for the entire CV stack.