Principal Machine Learning Engineer

5 days ago

New York, New York, United States Appgate Careers Full time $120,000 - $180,000 per year
About the Role

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.

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.
Minimum Qualifications
  • 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.
Preferred Qualifications
  • 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.
What Success Looks Like
  • 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.

Location: New York City

Department: AI / Fraud Prevention Engineering

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

Compensation: k + bonus

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