ML Engineer
Tasks
- Build and maintain inference monitoring pipelines and artifact monitoring tools
- Collaborate cross functional teams to align ML systems with business needs
- Develop and maintain feature engineering repositories
- Develop event based and price based features
- Develop static and temporal features
- Evaluate and track ML artifacts for coverage and feature alignment
- Extend and scale monitoring pipelines
- Implement production based adjusters for customer deployments
- Monitor and report ML model performance and explainability metrics
- Perform exploratory data analysis for data health
- Retrain models and automate MLOps pipelines
- Troubleshoot production ML workflows to root cause
Perks/Benefits
- N/A
Skills/Tech-stack
Airflow | CI/CD | Dagster | Data Analysis | Data Visualization | Docker | Exploratory Data Analysis | Feature Engineering | GCP | Kedro | Kubernetes | MLOps | Machine Learning | Model Explainability | Model Monitoring | NumPy | Pandas | Polars | PyTorch | Python | SQL | Scikit-learn | TensorFlow | Time Series
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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