Continental - Machine Learning Engineer - Python

Key Responsibilities :

- 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

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 to 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