Role Overview
We are seeking a Machine Learning Engineer specializing in Fraud Prevention to develop intelligent systems capable of detecting suspicious transactions, account abuse, payment fraud, and emerging financial threats. The role combines machine learning engineering, financial analytics, and production-grade technology.

Key Responsibilities

• Design and deploy machine-learning models for real-time fraud detection.
• Analyze transaction, behavioral, and customer data to identify fraudulent patterns.
• Develop feature-engineering pipelines for high-volume financial datasets.
• Build model-serving infrastructure capable of supporting low-latency transaction decisions.
• Monitor model accuracy, drift, false positives, and operational performance.
• Collaborate with fraud analysts and risk specialists to improve detection strategies.
• Develop experimentation frameworks for testing new fraud-detection approaches.
• Implement explainability and monitoring processes for production models.
• Continuously research emerging techniques in anomaly detection and financial crime analytics.

Qualifications & Experience

• 5+ years of experience in machine learning, data science, or applied AI.
• Strong Python and SQL programming skills.
• Practical experience with supervised and unsupervised machine-learning techniques.
• Experience deploying models into production environments.
• Strong understanding of fraud detection, anomaly detection, or financial transaction analytics.
• Experience with cloud infrastructure, MLOps, and real-time data systems.
• Knowledge of fintech, payments, or banking systems is strongly preferred.

What We Offer

• Opportunity to build AI systems protecting modern financial services.
• Access to complex, high-volume financial datasets.
• Advanced technical challenges across machine learning and fintech.
• Competitive compensation and strong opportunities for career growth.