MLOps Senior [Risk Team]
At a glance
Senior ML Platform & Ops role at Plata Card. Worldwide.
Pay not stated ML Platform & Ops salary
aimlroles summary, based on the employer's posting.
What you'll do
- Assess feature-store options, recommend a direction, and design how features move from experiments into production
- Guide engineering and platform teams through implementing the feature architecture and lifecycle
- Build and maintain training and release pipelines covering experiment records, model versions, validation, packaging, and production handoff
- Choose monitoring tools with data scientists to track model changes and feature freshness or quality, then define alerts and response steps
What you bring
- At least three years in ML engineering, data engineering, or DevOps, including hands-on production ML deployment and support
- Experience creating ML training workflows with experiment tracking and a model registry
- Strong Python skills, practical SQL ability, and enough ML framework knowledge to package, serve, and debug models
- You understand feature stores and train-to-serving consistency, and have used Docker with an ML workflow orchestrator
- English at B1 level or above for communication with an international team
Who this fits
This senior role suits someone who can build ML infrastructure and production workflows while partnering with data scientists and platform engineers. Relocation support is available for Cyprus, Serbia, Georgia, or Kazakhstan; you can work from an office or remotely.
From the employer
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.
- 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
- 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
- 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