Posted today · be early
MLOps Senior [Risk Team]
Banco Plata, S.A., Institución de Banca Múltiple
WorldwideremotePosted today
Skill Required
MLOps-EngineerMachine-Learning-EngineerData-EngineerSite-Reliability-EngineerSenior-MLOps-EngineerML-Risk-EngineerML-Model-Risk-SpecialistMachine Learningdata engineeringEngineeringObservabilitysimilar)buildingMLflowPythonDockerdesignDevOpsCI/CDDesign PatternsSQLandCICDFulltime
Key highlights
- 3+ years of experience in ML Engineering, Data Engineering, or DevOps
- B1 or higher English level
- Relocation support to Cyprus, Serbia, Georgia, or Kazakhstan
- Flexible work from one of our offices or remote
- 20 days of annual leave and paid sick leave
Role overview
We are looking for a Senior MLOps Engineer to join the Risk team, which is responsible for automated IT solutions in risk management processes. The team solves tasks for creating models and running inference in production in real time with specified SLA, integrates many external services, and makes decisions based only on data. The Data Science team develops scoring models, including neural network-based approaches, and the role is to industrialize the entire ML lifecycle by building ML infrastructure from scratch, from automated training pipelines to a unified feature platform serving both DWH research and real-time production inference.
Responsibilities
- 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
Requirements
- 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
Benefits
- Relocation support to one of our hubs — Cyprus, Serbia, Georgia or Kazakhstan — with assistance for the employee and their family
- Flexible work from one of our offices or remote
- HealthcareCoverage
- 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
Additional details
- 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
- Originally posted on Himalayas
