Computer Vision / ML Engineer

Your tasks

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.

The role will focus on model training and optimisation, dataset preparation, performance analysis, deployment support and ongoing monitoring of ML models in production.

Requirements

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.

• 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.

Nice to Have

• Experience with Docker and MLOps.
• Experience with experiment tracking and ML lifecycle management.
• Exposure to ONNX, TensorRT or GPU inference.
• Experience working with production Computer Vision models.
• Exposure to video or camera-based analytics.