Machine Learning Engineer
Tasks
- Automate workflows between ideation and running models
- Build and maintain APIs and services
- Build and maintain data pipelines
- Build retraining pipelines
- Design compute and orchestration for training and inference
- Document architecture runbooks and operational procedures
- Implement model and data versioning
- Monitor drift degradation and data quality
- Participate in code reviews sprint planning and technical design
- Set engineering standards for testing and code review
- Take models to production and own deployments
- Write clean, tested, documented code
Perks/Benefits
- N/A
Skills/Tech-stack
APIs | AWS | AWS Glue | AWS Lambda | AWS Step Functions | Agile | Amazon S3 | Amazon SageMaker | CI/CD | Code review | Containers | Data Pipelines | Data Quality | Docker | Git | Infrastructure as Code | MLOps | Machine Learning | Model Drift | Model Monitoring | Non-relational | Non-relational Databases | NumPy | Pandas | Python | Relational databases | SQL | Scikit-learn | Step Functions | Testing | “as-code”
Education
Regions
Countries
States
Cities
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