At Grape Up, 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
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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
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Read, review and refactor existing data science code in Python - adding tests, improving structure and migrating notebooks into a standardized, production-ready project template
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Build and maintain CI/CD pipelines and Databricks Workflows for batch training and inference, with results written back to Lakehouse tables
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Apply MLOps best practices consistently across projects: experiment tracking, model versioning and registry (MLflow, Unity Catalog), reproducibility and safe rollback
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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)
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Optimize batch inference pipelines for performance and cloud cost
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Contribute to shared templates, standards and ways of working of a growing central MLOps capability
Requirements
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Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Engineering or a related field
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4+ years of professional experience in MLOps, ML Engineering or software engineering with a strong focus on production ML systems
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Strong Python skills and solid software engineering practices - clean code, unit and integration testing (e.g., pytest), refactoring of existing codebases
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Strong hands-on experience with the ML lifecycle (experiment tracking, model registry, versioning, deployment), ideally with MLflow on Databricks.
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Solid understanding of machine learning fundamentals - able to discuss model behaviour, features and statistical properties with data scientists, not only the code
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Hands-on experience with Databricks (Workflows, notebooks, Unity Catalog) and MLflow (experiment tracking, model registry)
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Experience building CI/CD pipelines for ML projects (e.g., GitHub Actions, Azure DevOps, GitLab CI)
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Experience implementing model monitoring and observability for ML systems in production
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Experience with at least one cloud platform (AWS or Azure)
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Strong communication skills - comfortable working directly with data scientists and business stakeholders in an international environment
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Fluency in English, both written and spoken
Nice to have
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Databricks Certification
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Experience with Databricks Asset Bundles and infrastructure-as-code (e.g., Terraform)
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Experience optimizing Databricks batch workloads for performance and cost (e.g., PySpark, cluster configuration)
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Background in forecasting, pricing or optimization models (e.g., revenue management)
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Experience in the aviation, travel or e-commerce domain
Benefits
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Non-corporate work environment among experienced engineers
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Individual growth & development plan supported by cyclical feedback sessions
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Access to knowledge platforms (e.g. Pluralsight)
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Equipment of your choice
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Financing of conferences, external trainings, and certifications
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Language lessons (English, German and Polish for foreigners)
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LuxMed private medical care
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Weekly Lunch & Learn where we meet up in the office, lunch together, and share our knowledge
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Employee referral program
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Rewards for the success of the month and year awarded by our employees
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Special rewards for your years with us (G-Man)
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Integration activities
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