Machine Learning Engineer (4023)

Enabling safe and rewarding digital lives for genuine people, everywhere We make it our mission to ensure more genuine people have digital access to opportunities, and businesses have access to more genuine people. Our technology draws on diverse and reliable data to create a single point of truth for identity and address verification. With over 30 years of experience behind us our team and technology are focused on enabling safe and rewarding digital lives for everyone. Regardless of age, location or background, genuine people everywhere should be able to digitally prove who they are and where they live. About the team and role Global Fraud Solutions The team provides decision support solutions to address business objectives in risk prevention and fraud detection. We deliver software solutions and offer client support using our expertise and a client-focused approach. Machine Learning Engineer Working closely with Software Engineering, Product and Data Scientist teams, the Machine Learning Engineer will advance ML capabilities within the GFS fraud detection platforms. You will work on the delivery of the ML roadmap and building robust MLOps pipelines. You will apply your expertise in machine learning to real-world fraud detection challenges faced by banking and fintech customers globally. What you will do Design, develop, and deploy machine learning models for fraud and AML detection, supporting both batch and real-time transaction scoring scenarios. Build and maintain MLOps pipelines covering model training, validation, deployment, monitoring, and retraining workflows using modern tooling (e.g. MLflow, Tecton, or equivalent feature stores). Collaborate with data engineers to design feature engineering pipelines and maintain the Predator feature dictionary and sync mechanisms. Optimise model performance to meet strict latency and TPS targets required for real-time fraud decisioning. Conduct model validation, A/B testing, permutation importance analysis, and champion/challenger evaluations to ensure model quality. Work with the Architecture Review Committee (ARC) to align ML platform choices with the overall modernization architecture. Stay current with 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.