Machine Learning Engineer

• Develop full-stack machine learning models, including binary classification and regression, for financial-crime detection and prevention during transaction processing and client onboarding
• Gather requirements, prototype solutions, and deploy models in production
• Own and evolve the Dynamic Risk Score (DRS) across all markets, scoring the full client base daily
• Manage transaction rules with ML-driven detection and optimize performance and generated alerts
• Establish continuous monitoring of model and rule performance in production
• Develop a champion-challenger framework to keep the best-performing model live
• Participate in infrastructure development for ML model operationalization and inter-service interactions
• Serve and infer models of varying complexity
• Deploy, serve, monitor, and improve a production ML product that supports quarantine, alert suppression, and future credit decisions

Senior Machine Learning Engineer

• Design and build core machine learning systems for a proactive, forward-looking AI platform
• Own projects end-to-end, including data curation, model training, evaluation, inference, and continuous iteration
• Translate cutting-edge research concepts into robust, reliable production systems
• Use live production signals and data to troubleshoot model anomalies and system bottlenecks
• Deploy systems, analyze real-world performance metrics, refine approaches, and iterate rapidly
• Collaborate with research, engineering, and product teams to connect technical milestones with user value
• Mentor fellow engineers and conduct rigorous code and technical reviews
• Set high standards for technical judgment through hands-on execution
• Optimize model architectures across latency, computational cost, reliability, and safety at scale