Mlops engineer risk management

The Risk team develops automated IT solutions for risk management, creating models and running real-time production inference under specified SLAs while integrating external services and making data-driven decisions. The Data Science team develops scoring models, including neural network-based approaches.

• Evaluate Feature Store / Feature Registry solutions, prepare a recommendation, and design the architecture and feature lifecycle process from experiment to stable production
• Lead implementation by engineering and platform teams
• Design, build, and own ML training and deployment pipelines, including experiment tracking, model registry, model CI/CD, packaging, and handoff to production
• Select a platform such as MLflow or alternatives, establish versioning and validation standards, and continuously evolve the infrastructure
• Select tooling and set up monitoring for model quality and feature health in collaboration with the DS team
• Define the alerting and response process

• 3+ Years of experience in ML Engineering, Data Engineering, or Dev Ops, including hands-on deployment and maintenance of production ML systems
• Experience building ML training pipelines with experiment tracking and a model registry such as MLflow or W&B
• Understanding of feature store concepts and train-serve consistency challenges
• Strong Python skills and sufficient understanding of ML frameworks to package, serve, and debug models
• Experience with Docker and ML pipeline orchestration tools such as Kubeflow, Argo Workflows, or Metaflow
• Solid SQL skills and understanding of data warehouse architecture
• English B1 or higher

• Relocation support to one of the hubs in Cyprus, Serbia, Georgia, or Kazakhstan, including assistance for the employee and their family
• Healthcare coverage
• Education budget for language lessons, professional training, and certifications
• Wellness budget for mental health and fitness activity reimbursements
• 20 Days of annual leave and paid sick leave