• Own the end-to-end machine learning platform, including model development workflows, training, deployment, monitoring, retraining, and lifecycle management. • Define and execute the roadmap for scalable ML infrastructure supporting both real-time and batch use cases. • Lead applied machine learning initiatives supporting compliance and customer success, including areas such as: • Compliance monitoring, alerting, and investigation support • Customer success automation, prioritization, and insight generation • Internal operational tooling and responsible AI adoption • Partner with cross-functional stakeholders to translate complex business, customer, and regulatory problems into production ML solutions. • Build, mentor, and scale a small team of ML engineers and applied scientists, operating as a player-coach when needed.