Machine Learning Engineer - Fraud Risk
Builds scalable ML systems and end-to-end pipelines for fraud detection, anomaly detection, and real-time decisioning in payments. Requires 5+ years ML production experience, including 2+ in fraud/risk, Python proficiency, and ML frameworks like PyTorch/TensorFlow.
About the job
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
Skills
Python, PyTorch, TensorFlow, scikit-learn, Machine Learning, Supervised Learning, Unsupervised Learning, Anomaly Detection, Feature Engineering, Model Deployment, Drift Detection, Real-Time Ml, Fraud Detection
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