ML Infrastructure Engineer
Tasks
- Architect cloud and on prem ML training and evaluation
- Build data management pipelines for ingestion and storage
- Deliver projects end-to-end
- Design requirements for successful model deployment
- Develop cloud data infrastructure system
- Develop processes for transitioning models to production
- Evaluate methods for model deployment to test and production vehicles
- Implement model deployment systems for specialized hardware
- Mentor engineers
- Optimize model training code using better data formats
- Own infrastructure for deploying machine learning models
Perks/Benefits
- N/A
Skills/Tech-stack
Airflow | Amazon SageMaker | Argo | C++ | Cloud platform | Databricks | Dataproc | Google Cloud | Google Cloud Platform | Infrastructure as Code | Jetson | Kubernetes | MLOps | Machine Learning | Model Deployment | Model Monitoring | Node.js | ONNX | Python | ROS | Robotics | Spark | TensorRT | Terraform | Vertex AI | “as-code”
Education
N/A
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