ML Infrastructure Engineer
Tasks
- Architect cloud and on prem ML training and evaluation infrastructure
- Build data management pipelines for ingestion storage and training and evaluation
- Define requirements for successful model deployment
- Deliver projects end-to-end
- Develop cloud data infrastructure system
- Evaluate methods for model deployment to test and production vehicles
- Implement model deployment monitoring and MLOps
- Improve transition of models from research to production
- Optimize model training code for new data storage techniques
- Own infrastructure for machine learning model development and deployment
Perks/Benefits
- N/A
Skills/Tech-stack
Apache Airflow | Apache Spark | Argo Workflows | Data Ingestion | Data Pipelines | Data Storage | Databricks | Distributed Systems | Jetson | Kubernetes | MLOps | Machine Learning | Model Deployment | Node.js | ONNX | Python | SageMaker | TensorRT | Terraform | Vertex AI | Workflow Orchestration
Education
N/A
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