MLOps Senior [Risk Team] at Plata Card

Job 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

Requirements

Must-haves

Challenges That Await You
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
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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlci/cdpythondockersql

Platform: greenhouse
Effort: quick

Benefits

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Wellness