ML Engineer
Tasks
- Build inference monitoring pipelines
- Collaborate with cross-functional teams
- Develop feature engineering repositories
- Evaluate critical ML artifacts
- Extend monitoring pipelines
- Implement MLOps pipeline automation
- Implement production based adjusters
- Maintain artifact monitoring tools
- Maintain data health
- Monitor ML model performance
- Optimize MLOps pipelines
- Perform exploratory data analysis
- Report reliability and explainability metrics
- Retrain ML models
- Track explainability files coverage and feature alignment
- Troubleshoot production ML workflows
Perks/Benefits
- N/A
Skills/Tech-stack
Airflow | CI/CD | Dagster | Data Analysis | Data Visualization | Docker | Explainable AI | Exploratory Data Analysis | Feature Engineering | GCP | Kedro | Kubernetes | MLOps | Machine Learning | Model Monitoring | NumPy | Pandas | Polars | PyTorch | Python | SQL | Scikit-learn | TensorFlow | Time Series
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Regions
Countries
States
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