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
continental