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 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 / Certification : Degree in Computer Science, Data Science, Engineering, Mathematics, or related field Preferred certifications in Azure Data & AI, Databricks, or Experience : 3 to 5+ years of hands-on experience in applied machine learning, developing production-grade models for business use 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 Experience : No direct management responsibilities; expected to act as technical lead for ML within product / International Experience : Experience collaborating with globally distributed and cross-functional teams

(ref : hirist.tech)