ML Engineer
Summary
The ML Engineer will train, fine-tune, and deploy Computer Vision models while managing datasets and monitoring production performance. The role requires expertise in Python, deep learning frameworks, and MLOps practices to ensure model accuracy and reliability.
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.