Principal Machine Learning Engineer

We are seeking an exceptional Principal 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.

- 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.

- Real-time AI-driven fraud prevention models with measurable reduction in false positives and detection latency.

- Scalable, automated ML pipelines enable faster experimentation and deployment.

- Cross-functional collaboration delivering tangible business impact in fraud loss reduction.

- A culture of ML excellence, experimentation, and continuous learning across the team.

Experience: 5+ years (Staff) or 8+ years (Principal) in ML or fraud detection systems