Machine Learning Engineer – MLOps, Software Engineering

Responsibilities

• Work on the backend services behind our route-planning estimator and other operational estimators (e.g., process durations, driver availability) and ensure their stability and maintainability.
• Take responsibility for code quality and structure in our ML repositories (reviews, refactorings, architecture).
• Work closely with our Data Scientists and reliably bring models and feature pipelines into production.
• Develop and operate the associated data and training pipelines in Databricks.
• Build and maintain CI/CD pipelines in Azure DevOps – including automated tests and deployments.
• Ensure that our systems run reliably in the cloud environment, analyze production incidents and derive sustainable improvements for code and processes.

Requirements

• You have experience in professional software engineering and have independently contributed to production services or components: from implementation through reviews and tests to stable operation.
• You are highly proficient in Python in a production context and write structured, maintainable, and testable code.
• You have experience with automated testing (e.g., pytest) and ensure well-designed unit and integration tests for your services.
• You have experience with cloud environments and CI/CD.
• You understand fundamental ML concepts (training/inference paths, features, retraining, evaluation) well enough to understand our Data Scientists' models and pipelines and make them production-ready.
• You can explain technical topics appropriately for the audience and are comfortable working in a German-speaking environment; you use English confidently in technical contexts.

Core Competencies

Proficient in Python for backend services, with a strong focus on code quality, maintainability, and automated testing. Experienced in developing and operating data pipelines in cloud environments, particularly using Databricks and Azure DevOps.