Machine Learning Engineer

• Design, develop, and deploy machine learning models for fraud and AML detection
• Support batch and real-time transaction scoring scenarios
• Build and maintain MLOps pipelines for model training, validation, deployment, monitoring, and retraining
• Collaborate with data engineers on feature engineering pipelines and maintain the Predator feature dictionary and sync mechanisms
• Optimise model performance to meet latency and TPS targets for real-time fraud decisioning
• Conduct model validation, A/B testing, permutation importance analysis, and champion/challenger evaluations
• Work with the Architecture Review Committee to align ML platform choices with the modernization architecture
• Track advances in fraud detection ML, including graph-based models, anomaly detection, and generative AI applications, and propose relevant adoptions
• Mentor junior team members and contribute to knowledge sharing across squads