IT engineer Machine Learning

Job Description

• Develop and deliver robust machine learning solutions addressing diverse business challenges (forecasting, classification, optimization, automation) on the Azure Databricks platform.
• Own the full ML lifecycle: model development, deployment, monitoring, and retraining — supported by standardized infrastructure and DevOps practices.
• Apply strong mathematical and problem-solving skills to translate complex business requirements into effective ML models.
• Collaborate with Product Owners, data engineers, DevOps, and architecture teams to build scalable, maintainable, and governed ML pipelines.
• Demonstrate curiosity and an iterative mindset, exploring alternative modeling approaches to achieve satisfactory business outcomes.
• Reports to: Head of Data & Analytics IT Competence Center
• Collaborates with: Product Owners, data engineers, DevOps engineers, architecture/governance teams
• Location scope: Global business and IT teams
• Platform scope: Databricks (MLflow, notebooks, jobs, model registry), Azure services (Blob Storage, Key Vault, Event Hub, API Management)

Main Tasks

- Design, build, and evaluate ML models primarily in Python using libraries such as scikit-learn, XGBoost, Prophet, PyTorch, TensorFlow
- Perform feature engineering using pandas and PySpark where needed
- Collaborate with data engineers on data acquisition and pipeline integration

- Package and deploy models to production using MLflow’s Python API and CI/CD pipelines
- Manage model versioning, monitoring, and lifecycle workflows
- Build retraining pipelines and schedule model refreshes

- Integrate ML workflows with Azure-native services (Functions, Event Grid, API Management)
- Collaborate with DevOps engineers to automate deployments and enable observability
- Align with architecture and governance teams on standards compliance

- Advise Product Owners and business teams on feasibility, complexity, and architectural implications of ML solutions
- Translate business problems into viable ML models and workflows
- Support backlog prioritization and iterative development

- Write clean, reusable, testable code for ML pipelines using software engineering best practices
- Contribute to shared libraries and reusable components
- Apply version control, testing, and documentation standards

Qualifications

• Education / Certification:
  Degree in Computer Science, Data Science, Engineering, Mathematics, or related field
  Preferred certifications in Azure Data & AI, Databricks, or MLflow
• Professional Experience:
  3–5+ years of hands-on experience in applied machine learning, developing production-grade models for business use cases
• Project or Process Experience:
  Proven ability to translate business challenges into effective ML models, conduct experimentation, and iterate toward impact
  Experience working with large-scale structured data and integrating models into data pipelines
• Leadership Experience:
  No direct management responsibilities; expected to act as technical lead for ML within product teams
• Intercultural / International Experience:
  Experience collaborating with globally distributed and cross-functional teams

Additional Information

The well-being of our employees is important to us. That's why we offer exciting career prospects and support you in achieving a good work-life balance with additional benefits such as:

• Training opportunities
• Mobile and flexible working models
• Sabbaticals

and much more...

Sounds interesting for you?

are important to us and make our company strong and successful. We offer equal opportunities to everyone - regardless of age, gender, nationality, cultural background, disability, religion, ideology or sexual orientation.