ML Infrastructure Engineer
Tasks
- Architect cloud and on prem ML training infrastructure
- Build data management pipelines for ingestion storage and training
- Deliver projects end-to-end
- Deploy cloud and on prem ML evaluation infrastructure
- Develop cloud data infrastructure
- Develop systems to transition models from research to production
- Evaluate and implement model deployment methods for test and production
- Optimize model training code for storage techniques and formats
- Participate in model design research and set deployment requirements
Perks/Benefits
Skills/Tech-stack
Amazon SageMaker | Apache Airflow | Apache Spark | Argo | Cloud platform | Data pipeline | Databricks | Dataproc | Distributed Systems | Google Cloud | Google Cloud Platform | Jetson | Kubernetes | MLOps | Machine Learning | Model Monitoring | Node.js | ONNX | Python | TensorRT | Terraform | Vertex AI
Education
N/A
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