Senior MLOps Engineer

• Flexible work from one of our offices or remote
• Healthcare Coverage
• Education Budget: Language lessons, professional training and certifications
• Wellness Budget: Mental health and fitness activity reimbursements
• Vacation policy: 20 days of annual leave and paid sick leave

• Relocation support to one of our hubs - Cyprus, Serbia, Georgia or Kazakhstan - with assistance for the employee and their family

• 3+ years of experience in ML Engineering, Data Engineering, or DevOps with hands-on involvement in deploying and maintaining ML systems in production
• Experience building ML training pipelines with experiment tracking and model registry (MLflow, W&B, or similar)
• Understanding of feature store concepts and train-serve consistency challenges
• Strong Python skills; understanding of ML frameworks enough to package, serve, and debug models
• Experience with Docker and ML pipeline orchestration (Kubeflow, Argo Workflows, Metaflow, or similar)
• Solid SQL skills and understanding of data warehouse architecture

• B1 or higher English level for effective communication with an international team

We are looking for Senior MLOps Engineer to join our Risk team. Risk team is responsible for automated IT solutions in risk management processes. Within this process we solve the tasks for creating models and inference it on production in real-time with specified SLA, integrate tons of external services and make decisions only on data.

Our Data Science team develops scoring models including neural network-based approaches, and we need to industrialize the entire ML lifecycle. We are looking for an MLOps Engineer to build ML infrastructure from scratch - from automated training pipelines to a unified feature platform serving both DWH research and real-time production inference.

• Conduct evaluation of Feature Store / Feature Registry solutions (Feast, Tecton, or custom), prepare a recommendation, design the architecture and feature lifecycle process (experiment → stable → production). Lead the implementation by engineering and platform teams
• Design, build, and own ML training and deployment pipelines: experiment tracking, model registry, CI/CD for models, packaging and handoff to production. Select the platform (MLflow or alternatives), establish versioning and validation standards, and evolve the infrastructure continuously
• Select tooling and set up monitoring of model quality (drift, degradation) and feature health (freshness, data quality) in collaboration with the DS team. Define the alerting and response process