ML Engineer
Tasks
- Automate MLOps pipelines
- Build feature engineering repositories
- Build inference monitoring pipelines
- Build internal tooling
- Collaborate with cross-functional teams
- Develop static features
- Develop temporal features
- Engineer event based features
- Engineer price related features
- Engineer seasonality features
- Evaluate explainability outputs
- Extend model monitoring pipelines
- Implement production based adjusters
- Monitor ML artifacts
- Monitor model performance
- Optimize MLOps pipelines
- Perform exploratory data analysis
- Report explainability metrics
- Report reliability metrics
- Retrain machine learning models
- Support production ML workflows
- Track feature alignment metrics
- Track feature coverage metrics
- Troubleshoot production ML issues
Perks/Benefits
- N/A
Skills/Tech-stack
Airflow | CI/CD | Cloud platform | Dagster | Data Analysis | Data Visualization | Demand forecasting | Docker | Exploratory Data Analysis | Feature Engineering | Google Cloud | Google Cloud Platform | Kedro | Kubernetes | ML observability | MLOps | Model Explainability | Model Monitoring | NumPy | Pandas | Polars | Pricing | PyTorch | Python | SQL | Scikit-learn | TensorFlow | Time Series
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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