AI Engineer - Computer Vision

Responsibilities:
The core on-device CV stack detection, tracking and per-feature analytics engineered to run reliably within the compute and memory budget of edge hardware, at production quality across a growing device fleet.
Model performance on the edge: taking models built for the GPU and making them fast and accurate on constrained devices: NCNN/ONNX export, int8 quantisation, resolution/cadence tuning, and a shared-inference design that lets one box serve many features.
The analytics feature set: footfall & demographics, dwell and zone/table occupancy, queue and service-time, PPE/SOP compliance, and cross-camera person re-identification.
Accuracy in the real world: building the evaluation harnesses and ground-truth workflows that hold every feature to a measurable bar across varied camera angles, lighting and occlusion, rather than trusting benchmark FPS.
The path from model to fleet, partnering with the platform team on deployment, OTA model updates and monitoring so accuracy holds across every store, not just the lab. A voice in the CV roadmap and engineering standards: which models we invest in, how we evaluate, how we ship and helping level up the vision engineers around you.
Requirements:
3+ years of computer-vision / deep-learning engineering with systems that reached production.
Deep hands-on with object detection (YOLO family) and multi-object tracking (ByteTrack, DeepSORT or similar) on real camera / RTSP video.
Real experience optimising and deploying models for inference ONNX, quantisation (int8), and at least one edge/runtime stack (NCNN, TensorRT, OpenVINO or TFLite) on devices like Raspberry Pi or Jetson.
Strong Python and OpenCV, and a genuine feel for the accuracy-latency-cost trade-off with the rigour to measure it.
Ownership instinct: you can take a fuzzy problem to a shipped, monitored feature and hold yourself to production standards.
Strong pluses:
Person re-ID, pose, fine-grained age/gender, or vision-language models for scene understanding.
Edge accelerators (Hailo, Coral, Jetson), Linux/systemd, containers, and an MLOps mindset (CI, model registry, OTA).
Retail, surveillance or smart-camera analytics domain experience.
Building annotation pipelines and evaluation frameworks.

Experience
3-6 yrs