IT engineer Machine Learning
IT engineer Machine Learning
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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 MLflows 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:
35 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. Thats 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 Click here to find out more.
Diversity Inclusion & Belonging 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.
Ready to drive with Continental Take the first step and fill in the online application.
Remote Work :
No
Employment Type :
Full-time
Employment Type
Full-time
