Machine Learning Engineer - Fraud Risk

Responsibilities

• Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis
• Develop and maintain end-to-end ML pipelines: data ingestion, feature engineering, model training, deployment, and continuous monitoring
• Design and implement low-latency, real-time decision systems partnering with fraud risk data scientists, integrating with transaction or behavioral data streams
• Own ML infrastructure, including model versioning, automated retraining, and safe deployment strategies (e.g., shadow, rollback)
• Build robust monitoring and alerting for model performance, latency, data quality, and drift
• Lead experimentation on model explainability, drift detection, and adversarial robustness for fraud prevention use cases
• Develop tooling and processes to improve the effectiveness and speed of the ML development lifecycle
• Partner with platform teams to meet strict SLAs for availability, latency, and accuracy
• Collaborate closely with talented engineers, data scientist and compliance teams across Rain

Requirements

• 5+ years of experience building ML systems in production; at least 2+ in fraud, risk, or anomaly detection domains
• A degree in Computer Science, Engineering, Statistics, Applied Math, or a related technical field
• Proven track record designing and maintaining ML models at scale
• Advanced proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn)
• Strong understanding of supervised/unsupervised learning, anomaly detection, and statistical modeling
• Ability to work autonomously, manage ambiguity, and collaborate closely with data scientists to translate analytical models into robust fraud prevention systems
• Experience developing, validating, and productionalizing predictive real-time and offline fraud detection models using supervised and unsupervised ML techniques
• Experience collaborating with cross-functional teams to prioritize, scope, and deploy MLI solutions at scale

Nice to Haves

• Domain expertise in banking, payments, or transaction monitoring
• Experience with graph-based or network-level fraud detection techniques
• A graduate degree in Computer Science, Engineering, Statistics, Applied Math, or a related technical field
• Experience fine-tuning or adapting generative AI / large language models for pattern generation or synthetic data augmentation (in partnership with data science)
• Knowledge of model governance, bias mitigation, and regulatory compliance in fraud contexts