Machine Learning Engineer

Overview: We’re hiring a Machine Learning Engineer to design, build, and deploy production-grade ML solutions within a regulated banking environment. This role focuses on delivering reliable, explainable, and scalable models that support areas such as fraud detection, risk, credit, AML, customer analytics, and operational efficiency. You’ll work closely with data science, engineering, risk, and compliance teams to ensure models meet both business objectives and regulatory standards. Key Responsibilities: • Design, develop, and deploy machine learning models into production banking systems • Build and maintain end-to-end ML pipelines (data ingestion, training, validation, inference) • Partner with data scientists to productionize models with a focus on stability and explainability • Implement model monitoring, drift detection, retraining strategies, and performance reporting • Work with large-scale transactional and customer datasets • Ensure ML solutions comply with model risk management (MRM) and regulatory requirements • Collaborate with engineering teams to integrate models via APIs and services • Produce clear documentation for audits, governance, and stakeholder review Required Skills & Experience: • Strong Python experience and ML libraries (scikit-learn, PyTorch, TensorFlow) • Solid understanding of ML fundamentals, feature engineering, and model evaluation • Experience deploying ML models in production environments • Hands-on experience with cloud platforms (AWS, Azure, or GCP) • Strong SQL skills and experience working with structured financial data • Familiarity with CI/CD, version control, and software engineering best practices • Experience working in regulated or enterprise environments