Aviation
2-5
Senior
Software Engineering
Tech stack
Python
MLflow
CI/CD for ML
Cloud (AWS or Azure)
Machine Learning fundamentals
Databricks (Workflows, Unity Catalog, Lakehouse Monitoring)
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
- 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
Recruitment owner
Klaudia Jazowska
Senior MLOps Engineer (Databricks)
apply now
Benefits
Employee referral
Knowledge platforms
Equipment
Integration activities
Employee referral
Knowledge platforms
Equipment
Integration activities
Lunch & Learn
In-house Tech Up
Language lessons
Lunch & Learn
In-house Tech Up
Language lessons
Conferences & training
Development plan
Feedback sessions
Business travel opportunities
Conferences & training
Development plan
Feedback sessions
Business travel opportunities
Check out the next steps of your recruitment process
Application
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HR & technical interview
Let’s talk about your experience and expectations.
Meeting with the Manager
Learn more about the project you could be involved in.
Offer
We’re excited to welcome you onboard! Let’s discuss the details.
Feedback
If you’re not lucky this time, we’ll tell you what you can improve to join us in the future.
