# MLOps Senior [Risk Team] at Plata Card

MLOps Senior [Risk Team] at Plata Card in Worldwide. Remote. Posted February 12, 2026.

- Company: Plata Card
- Location: Worldwide
- Workplace: Remote
- Experience: 3+ years
- Posted: 2026-02-12
- Apply: https://plata.careers/vacancy/details?id=5118558008&gh_jid=5118558008
- Source: https://jobmagic.ai/jobs/mlops-senior-risk-team-plata-card-wm0kby

## Description

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.

**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, Georgia 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:** Annual leave and paid sick leave as per applicable policies