MLOps Senior [Risk Team]

remote work; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Relocation support

Flexible work

Healthcare coverage

Education budget

Wellness budget

Vacation policy

Job summary

Remote Work in the United States is seeking a Senior MLOps Engineer to join the Risk team. The Riskteam focuses on automated IT solutions in risk management, building models and real-time inference with SLA.

You will build ML infrastructure from scratch, from automated training pipelines to a unified feature platform serving both research and production inference. You will evaluate feature stores, select platforms, and establish versioning and validation standards across the ML lifecycle.

Qualifications

• 3+ years of experience in ML Engineering, Data Engineering, or DevOps in production ML systems.
• Experience with MLflow, W&B, or similar for experiment tracking.
• Strong Python skills and knowledge of ML frameworks for packaging and serving.
• Understanding feature store concepts and train-serve consistency.

Responsibilities

• Build ML training pipelines and deployment workflows.
• Design feature store lifecycle from experiment to production.
• Implement CI/CD, model registry, and serving pipelines.

Skills

ML Engineering

Data Engineering

Python

Docker

Kubeflow

Argo Workflows

MLflow

SQL

Feature stores

English (B1+)

Tools

MLflow

Weights & Biases

Kubeflow

Job description

We are looking for Senior MLOps Engineer to join our Risk team. Riskteam 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 workfrom 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: Annual leave and paid sick leave as per applicable policies