Machine Learning Engineer (4023)
Core
Design, develop, and deploy machine learning models for fraud and AML detection in batch and real-time transaction scoring scenarios.
Role type
Machine Learning Engineer (Fraud Detection)
Builds
Fraud detection and AML decision support software solutions for banking and fintech customers
Domain
Financial services / Fraud prevention / Anti-Money Laundering
Deliverable
production ML models
Required skills
Python, cloud-native ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI), containerisation (Docker, Kubernetes), CI/CD for ML pipelines, fraud detection and AML models, model validation, A/B testing, feature engineering
Preferred skills
graph-based models, anomaly detection, generative AI applications
Technologies
MLflow, Tecton, feature stores
Responsibilities
Design and deploy ML models for fraud/AML detection; Build and maintain MLOps pipelines; Collaborate on feature engineering pipelines; Optimise model performance for latency/TPS targets; Conduct model validation and A/B testing; Align ML platform choices with architecture; Mentor junior team members
Seniority
Mid-level, hands-on IC
