Staff Machine Learning Engineer

We are seeking an exceptional Staff Machine Learning Engineer to lead the design and development of the next generation of our AI-driven fraud detection platform.

 You will architect large-scale ML systems that detect and prevent fraud in real time combining deep machine learning expertise with scalable engineering and domain knowledge in financial systems.

This is a hands-on technical leadership role, shaping our fraud prevention roadmap and ensuring the platform evolves to meet emerging threat patterns through automation, data intelligence, and generative AI–enhanced detection models.

 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 monitoring.
• Leverage modern AI techniques, including generative AI, to improve fraud pattern discovery and model robustness.
• Design and implement real-time decision systems, integrating with transaction or behavioral data streams.
• Collaborate closely with engineering, security, and risk teams to define data strategy and labeling frameworks.
• Lead experimentation on model explainability, drift detection, and adversarial robustness for fraud prevention use cases.
• Promote engineering excellence — automation, CI/CD, reproducibility, observability, and model governance.
• Mentor and guide ML and software engineers, fostering best practices and innovation.

• 5 years of experience building ML or AI systems in production; at least 2 in fraud, risk, or anomaly detection domains.
• Proven track record designing and maintaining ML pipelines at scale.
• Expertise in Python, ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn), and CI/CD (GitHub Actions, Jenkins, or similar).
• Strong understanding of supervised / unsupervised learning, anomaly detection, and statistical modeling.
• Experience with big data and distributed systems (e.g., Spark, Kafka, Flink, or similar).
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerized deployments (Docker, Kubernetes).
• Strong collaboration, communication, and cross-team leadership skills.

• Prior experience with fraud or financial crime detection, identity verification, or risk scoring systems.
• Domain expertise in banking, payments, or transaction monitoring
• Experience fine-tuning or adapting generative AI / large language models for pattern generation or synthetic data augmentation.
• Familiarity with streaming analytics, graph ML, or time-series anomaly detection.
• Knowledge of model governance, bias mitigation, and regulatory compliance in fraud contexts.
• Contributions to fraud detection research, open-source, or AI publications.