mlops engineer

Wise is a global technology company that provides a platform for international money transfers and financial management. The organization focuses on reducing fees and increasing the speed of cross-border payments for individuals and businesses. The Risk ML team develops infrastructure and model lifecycle platforms specifically for financial crime detection, enabling scalable and reproducible machine learning operations.

• Design and build a declarative training pipeline for standardized, config-driven model training;
• Build model packaging and serving abstractions to provide a unified interface for various model types;
• Implement a model evaluation framework including standardized metrics and automated validation gates;
• Build model monitoring systems for drift detection, performance alerts, and automated retraining triggers;
• Own the integration layer with central ML infrastructure to ensure FinCrime-specific tooling aligns with shared foundations;
• Maximize data science productivity by shifting focus from operational maintenance to detection performance.

• Experience building ML platform infrastructure in production, such as training pipelines, model serving, or monitoring systems;
• Strong software engineering fundamentals with proficiency in Python, Kotlin/Java, and SQL;
• Experience with ML orchestration tools like Airflow or Kubeflow, model registries, and container-based deployment;
• End-to-end understanding of the ML lifecycle from data ingestion to monitoring;
• Product mindset for internal tooling with a focus on user adoption;
• Nice to have: Model serving at scale, experience in FinCrime/fraud/AML or regulated environments, experience with model monitoring and drift detection, track record of migrating teams from manual to platform-based workflows.

• Starting salary: £111,000 - £145,000 per year + RSUs;
• Office role with relocation support.