Senior MLOps Engineer (Databricks)

Quick Facts

Senior MLOps Engineer (Databricks) focused on taking ML models from notebooks to monitored, cost-efficient production workloads using Databricks.

Description

Join a central MLOps capability supporting forecasting, pricing, and optimization model deployments across commercial, revenue management, and operations. You will work with data scientists to move experiments into reliable production batch training and inference on Databricks, with proper monitoring, experiment tracking, versioning, and safe rollback.

Responsibilities

• Partner with data scientists to take models and algorithms from experiments/notebooks into reliable production workloads
• Read, review, and refactor existing data science code in Python; add tests, improve structure, and migrate notebooks into a standardized production template
• Build and maintain CI/CD pipelines and Databricks Workflows for batch training and inference; write results back to Lakehouse tables
• Apply MLOps best practices: experiment tracking, model versioning and registry (MLflow, Unity Catalog), reproducibility, and safe rollback
• Design model-specific monitoring strategies (including Databricks Lakehouse Monitoring)
• Optimize batch inference pipelines for performance and cloud cost
• Contribute to shared templates, standards, and ways of working for the central MLOps capability

Requirements

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Engineering, or related field
• 4+ years of professional experience in MLOps, ML Engineering, or software engineering with a strong focus on production ML systems
• Strong Python skills and software engineering practices (clean code, unit/integration testing such as pytest, refactoring)
• Hands-on experience with the ML lifecycle (experiment tracking, model registry/versioning, deployment), ideally with MLflow on Databricks
• Solid understanding of machine learning fundamentals (model behavior, features, statistical properties)
• Hands-on experience with Databricks (Workflows, notebooks, Unity Catalog) and MLflow (experiment tracking, model registry)
• Experience building CI/CD pipelines for ML projects (e.g., GitHub Actions, Azure DevOps, GitLab CI)
• Experience implementing model monitoring and observability in production
• Experience with at least one cloud platform (AWS or Azure)
• Strong communication skills working with data scientists and business stakeholders in an international environment
• Fluency in English (written and spoken)

Benefits

• Non-corporate work environment among experienced engineers
• Individual growth & development plan with cyclical feedback sessions
• Access to knowledge platforms (e.g., Pluralsight)
• Equipment of your choice
• Financing of conferences, external trainings, and certifications
• Language lessons (English, German, and Polish for foreigners)
• LuxMed private medical care
• Weekly Lunch & Learn sessions in the office
• Employee referral program
• Rewards for monthly and yearly success
• Special rewards for years with the company
• Integration activities