Machine Learning Engineer
Core
Designing end-to-end ML systems for real-time fraud detection and AML operations, unifying data across risk teams to stop fraud and prevent AI-driven attacks.
Role type
Machine Learning Engineer (Fraud Detection & Risk)
Builds
Production ML models, data pipelines, and backend services for fraud detection systems.
Domain
Financial Crime, Fraud Prevention, AML
Deliverable
production ML models
Required skills
Applied ML (PyTorch, Scikit-learn), SQL, End-to-end ML systems (feature pipelines, deployment, monitoring), Backend systems (Go), Security & privacy compliance
Preferred skills
Fraud/risk/cybersecurity domain knowledge, Software Engineering background, CI/CD, Docker, Kubernetes, Modern browser APIs, High-entropy data collection
Technologies
PyTorch, Scikit-learn, Go, SQL, Docker, Kubernetes
Responsibilities
Build and optimize data pipelines and backend services for real-time data processing; Develop and deploy ML models for fraud detection; Turn raw data into production-ready features; Collaborate with platform and backend engineers; Maintain security, privacy, and compliance standards; Champion testing, documentation, and observability best practices
Seniority
Mid-level, hands-on IC
