Machine Learning Engineer (Computer Vision)

Duties and

Train, fine-tune and evaluate Computer Vision models. Prepare and maintain datasets, experiments, evaluation results and model artefacts. Investigate false detections, missed events, tracking errors and model degradation.

Identify edge cases, define labelling tasks and review annotation quality. Analyse conditions such as layout, lighting, camera angle and crowding that may affect performance. Support model packaging, versioning, deployment, release and rollback activities.

Perform retraining, threshold tuning and regression testing when required. Maintain model documentation, evaluation reports, limitations and handover materials. Monitor model in production and maintain the model performance.

Follow privacy, security, data-retention and AI-governance requirements. Minimum Required Experience and

Practical Python, deep-learning and Computer Vision experience. Experience with at least one Computer Vision workflow such as detection, classification, tracking, pose estimation or video analytics. Familiarity with frameworks such as PyTorch, TensorFlow, OpenCV or similar tools.

Understanding of dataset preparation, model training, precision, recall, F1 and latency. Able to analyse model failures and recommend data, configuration or model improvements. Familiarity with annotation tools and labelling quality checks.

Experience with Docker, MLOps, experiment tracking, ONNX, TensorRT or GPU inference is useful but not mandatory. Camera-base