ML Engineer

Scope of Work/ Duties and Responsibilities 

• 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 Qualifications 

• 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, OpenCVor 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. 
• Good documentation and communication skills. 
• Experience with Docker, MLOps, experiment tracking, ONNX, TensorRT or GPU inference is useful but not mandatory. 
• Hospitality, F&B or camera-based analytics experience is useful but not mandatory.