ML Infrastructure Engineer
Tasks
- Architect and deploy cloud and on prem ML training and evaluation
- Build data management pipelines for ingestion storage and training
- Define model design requirements for deployment
- Deliver projects end-to-end
- Evaluate and deploy model deployment methods for test and production
- Improve research to production model transition with cost control
- Mentor or manage engineers when needed
- Own ML infrastructure
- Update model training code for data storage formats
Perks/Benefits
- N/A
Skills/Tech-stack
Airflow | Amazon SageMaker | Apache Spark | Argo | Databricks | Dataproc | Google Cloud | Google Cloud Vertex | Google Cloud Vertex AI | Infrastructure as Code | Kubernetes | MLOps | Machine Learning | Model Monitoring | NVIDIA Jetson | Node.js | ONNX | Python | TensorRT | Terraform | Vertex AI | Workflow Orchestration | “as-code”
Education
N/A
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