Senior MLOps Engineer (Databricks)

Expected, Python, GitHub Actions, GitLab, Databricks, Git, MLflow, CI/CD for ML, Cloud (AWS or Azure), Machine Learning fundamentals

Optional, Terraform, PyTorch

Operating system, Windows, macOS

About the project, 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.

This is how we work, at the client's site, you have influence on the choice of tools and technologies, you have influence on the technological solutions applied, you have influence on the product, you focus on product development, you focus on code maintenance, agile, scrum, kanban

Team members, fullstack developer, technical leader, data scientist, project manager

Your 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

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

Optional, 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

What we offer, 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

This is how we work on a project, Clean Code, code quality measures, code review, design patterns, architect / technical leader support, Continuous Deployment, Continuous Integration, documentation, issue tracking tools, functional tests, integration tests, performance tests, regression tests, test automation, testing environments, unit tests, manual tests

Development opportunities we offer, mentoring, substantive support from technological leaders, support of IT events, technical knowledge exchange within the company, time for development of your ideas

Benefits, private medical care, sharing the costs of foreign language classes, sharing the costs of professional training & courses, remote work opportunities, flexible working time, fruits, integration events, corporate library, no dress code, video games at work, coffee / tea, drinks, parking space for employees, leisure zone, extra social benefits, baby layette, employee referral program, charity initiatives

Recruitment stages, HR telephone interview, Technical & soft skills interview meeting, Meeting with the Manager, Offer, Feedback

Grape Up Sp. z o.o., Hi, we are Grape Up!, Our company provides technology consulting and modern software development services for mission-critical products. We specialize in delivering innovative solutions powered by cloud and AI.

This is how we work,