Computer Vision Engineer

Develop, evaluate and improve Computer Vision and machine-learning solutions using the existing codebase and delivery approach.Scope of Work Train, fine-tune and evaluate Computer Vision modelsPrepare and maintain datasets, experiments, evaluation results and model artefactsInvestigate false detections, missed events, tracking errors and model degradationIdentify edge cases, define labelling tasks and review annotation qualityAnalyse conditions such as layout, lighting, camera angle and crowding that may affect performanceSupport model packaging, versioning, deployment, release and rollback activitiesPerform retraining, threshold tuning and regression testing when requiredMaintain model documentation, evaluation reports, limitations and handover materials.Monitor model in production and maintain the model performanceFollow organization's privacy, security, data-retention and AI-governance requirementsMinimum Required Experience and Qualifications Practical Python, deep-learning and Computer Vision experienceExperience with at least one Computer Vision workflow such as detection, classification, tracking, pose estimation or video analyticsFamiliarity with frameworks such as PyTorch, TensorFlow, OpenCV or similar toolsUnderstanding of dataset preparation, model training, precision, recall, F1 and latencyAble to analyse model failures and recommend data, configuration or model improvements.Familiarity with annotation tools and labelling quality checksGood documentation and communication skillsExperience with Docker, MLOps, experiment tracking, ONNX, TensorRT or GPU inference is useful but not mandatoryHospitality, F&B or camera-based analytics experience is useful but not mandatory