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.