MLOps Senior [Risk Team]

Role overview We are hiring a Senior MLOps Engineer to industrialize the full machine-learning lifecycle for a risk management group that builds and serves scoring models, including neural-network approaches, in real time under strict SLAs. The role covers evaluating feature-store options, designing training and deployment infrastructure, and establishing production-grade monitoring, all in close partnership with data science, engineering, and platform teams.

Responsibilities - Evaluate Feature Store and Feature Registry solutions, recommend an architecture, and lead implementation of the feature lifecycle from experiment through production. - Design, build, and own ML training and deployment pipelines, including experiment tracking, model registry, CI/CD for models, packaging, versioning, validation, and production handoff. - Set up tooling and processes to monitor model quality (drift, degradation) and feature health (freshness, data quality), and define the alerting and response workflow. - Standardize the platform with continuous improvements as ML systems and use cases evolve. - Collaborate with Data Science, DWH, and production engineering to ensure train-serve consistency and reliable real-time inference.

Requirements - Three or more years in ML engineering, data engineering, or DevOps with hands-on production ML deployment experience. - Practical experience building training pipelines with experiment tracking and a model registry such as MLflow or Weights & Biases. - Working knowledge of feature-store concepts and train-serve consistency challenges. - Strong Python skills plus enough 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 familiarity with data warehouse architecture, plus B1 or higher English for an international team.

Benefits and work setup - Relocation support to hubs in Cyprus, Serbia, Georgia, or Kazakhstan, with assistance for the employee and family. - Flexible work from one of the offices or fully remote. - Healthcare coverage, an education budget for language lessons and certifications, and a wellness budget for mental health and fitness. - Twenty days of annual leave plus paid sick leave, in a culture that emphasizes innovation, honest feedback, and team celebration.