Machine Learning Engineer

Computer Vision / ML Engineer

About the Role

We are looking for a Computer Vision / ML Engineer to develop, evaluate and improve production-ready Computer Vision and Machine Learning solutions.

Key Responsibilities

• Train, fine-tune and evaluate Computer Vision and Machine Learning models.
• Prepare and maintain datasets, experiments, evaluation results and model artefacts.
• Investigate false detections, missed events, tracking errors and model degradation.
• Identify edge cases and define data-labelling and annotation requirements.
• Review annotation quality and identify opportunities to improve training data.
• Analyse environmental factors such as lighting, camera angles, layouts and crowding that may impact model performance.
• Perform model retraining, threshold tuning and regression testing when required.
• Support model packaging, versioning, deployment, release and rollback activities.
• Monitor models in production and help maintain model performance.
• Maintain technical documentation, evaluation reports, model limitations and handover materials.
• Follow applicable privacy, security, data-retention and AI-governance requirements.

Requirements

• 2–6 years of relevant experience in Machine Learning, Computer Vision, AI Engineering or related areas.
• Diploma or Degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field
• Strong hands-on experience with Python, deep learning and Computer Vision.
• Experience in at least one Computer Vision area such as:
• Object detection
• Image classification
• Object tracking
• Pose estimation
• Video analytics
• Experience with frameworks and tools such as PyTorch, TensorFlow, OpenCV or similar.
• Good understanding of dataset preparation and model training.
• Understanding of ML performance metrics including precision, recall, F1 score and latency.
• Ability to analyse model failures and recommend improvements to data, configuration or models.
• Familiarity with annotation tools and labelling quality checks.
• Good documentation, analytical and communication skills.