Senior Machine Learning Infrastructure Engineer
Tasks
- Build data pipelines for machine learning workloads
- Deploy and monitor ML models at scale
- Design scalable ML training and inference systems
- Ensure ML platform monitoring logging and alerting
- Implement distributed systems and storage solutions
- Implement model versioning
- Improve CI CD workflows for ML models and infrastructure
- Integrate cloud native tools and frameworks
- Manage large GPU clusters
- Mentor engineers
- Set up experiment tracking
Perks/Benefits
Skills/Tech-stack
AWS | Apache Airflow | C++ | CI/CD | Cloud platform | Databases | Distributed Systems | Docker | Experiment tracking | GPU clusters | Google Cloud | Google Cloud Platform | Kubeflow | Kubernetes | MLflow | Model versioning | Prefect | PyTorch | Python | Ray | SQL | Storage Systems | TensorFlow
Education
Regions
Countries
States
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