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.

Challenges That Await You

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

What Makes You a Great Fit

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

Our ways of working:

Innovative Spirit: A commitment to creativity and groundbreaking solutions
Honest Feedback: valuing open, transparent communication
Supportive Team: a strong, collaborative community
Celebrating Achievements: recognizing our wins together
High-Tech Environment: a team full of smart and revolutionary people who date to challenge the status quo of incumbent finances

Our benefits:

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

MLOps Engineer jobs

DevOps Engineer/ MLOps

Senior MLOps

ML Engineer (MLOps, Risk Team]

DevOps Engineer/ MLOps

Senior MLOps

ML Engineer (MLOps, Risk Team]