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 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 organization's 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, 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

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