Machine Learning Engineer - Fraud & Identity Security

Ziverge is an engineer-led software consultancy that helps organizations build high-reliability systems using JVM technologies, cloud-native architectures, and proven engineering practices. The Machine Learning Engineer will develop and deploy production-critical models for identity security and fraud prevention, owning the full machine learning lifecycle from data exploration and model development through deployment, monitoring, and operational support.

Responsibilities:

• Understand business objectives and develop models that help identify and prevent credential stuffing, account takeover (ATO), and other fraud behaviors, along with metrics to track progress
• Explore and visualize data to understand the problem space, identify differences in data distribution, and select suitable ML algorithms
• Apply machine learning, statistics, and data mining to improve efficiency across every aspect of identity security
• Develop scalable and efficient methods for large-scale data analysis and model development
• Define data augmentation pipelines, train models and tune hyperparameters, deploy models to production, and monitor and evaluate ML model performance
• Investigate and resolve production issues, contributing to the ongoing reliability of deployed models
• Collaborate with developers, program managers, and product managers in an open, creative environment
• Analyze feature requirements, assess technical feasibility, and provide clear estimates and risk assessments
• Write technical proposals and architectural documentation for new features and system changes
• Plan and implement work across epics and user stories, from initial design through to deployment and support
• Participate in code reviews, share knowledge with teammates, and contribute to a culture of continuous learning and improvement