Machine Learning Engineer
Core
Design and build end-to-end ML systems for real-time fraud detection, unifying data across risk teams to stop fraud and prevent AI-driven attacks.
Role type
Senior 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), Data engineering
Preferred skills
Fraud/risk/cybersecurity domain knowledge, Software Engineering background, CI/CD, Docker, Kubernetes, Browser APIs, LLMs for automation
Technologies
PyTorch, Scikit-learn, Go, SQL, Docker, Kubernetes
Responsibilities
Build and optimize data pipelines for real-time device and behavioral data processing; Develop and deploy ML models for fraud detection; Turn raw data into production-ready features; Collaborate with platform and backend engineers to integrate models; Maintain security, privacy, and compliance standards; Champion best practices in testing, documentation, and observability
Seniority
Senior, hands-on IC
