At ••••••••, we transform businesses by unlocking the potential of AI and data through innovative software solutions.

We partner with a leading European airline to bring its machine learning models into reliable, production-grade operation. You will join a central MLOps capability that is deployed into product teams across the commercial, revenue management and operations domains - helping data scientists take forecasting, pricing and optimization models from notebooks to monitored, cost-efficient production workloads on Databricks.

Responsibilities

  • Partner with data scientists across commercial, revenue management and operations teams to take their models and algorithms from experiments and notebooks into reliable production workloads

  • Read, review and refactor existing data science code in Python - adding tests, improving structure and migrating notebooks into a standardized, production-ready project template

  • Build and maintain CI/CD pipelines and Databricks Workflows for batch training and inference, with results written back to Lakehouse tables

  • Apply MLOps best practices consistently across projects: experiment tracking, model versioning and registry (MLflow, Unity Catalog), reproducibility and safe rollback

  • Design model-specific monitoring strategies together with data scientists - going beyond generic metrics to track the parameters, coefficients and data signals that matter for each model (incl. Databricks Lakehouse Monitoring)

  • Optimize batch inference pipelines for performance and cloud cost

  • Contribute to shared templates, standards and ways of working of a growing central MLOps capability

Requirements

  • Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Engineering or a 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 solid software engineering practices - clean code, unit and integration testing (e.g., pytest), refactoring of existing codebases

  • Strong hands-on experience with the ML lifecycle (experiment tracking, model registry, versioning, deployment), ideally with MLflow on Databricks.

  • Solid understanding of machine learning fundamentals - able to discuss model behaviour, features and statistical properties with data scientists, not only the code

  • 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 for ML systems in production

  • Experience with at least one cloud platform (AWS or Azure)

  • Strong communication skills - comfortable working directly with data scientists and business stakeholders in an international environment

  • Fluency in English, both written and spoken

Nice to have

  • Databricks Certification

  • Experience with Databricks Asset Bundles and infrastructure-as-code (e.g., Terraform)

  • Experience optimizing Databricks batch workloads for performance and cost (e.g., PySpark, cluster configuration)

  • Background in forecasting, pricing or optimization models (e.g., revenue management)

  • Experience in the aviation, travel or e-commerce domain

Benefits

  • Non-corporate work environment among experienced engineers

  • Individual growth & development plan supported by 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 where we meet up in the office, lunch together, and share our knowledge

  • Employee referral program

  • Rewards for the success of the month and year awarded by our employees

  • Special rewards for your years with us (G-Man)

  • Integration activities