ML Infrastructure Engineer
Tasks
- Architect and deploy cloud and on prem ML infrastructure
- Develop systems and processes for end to end ML projects
- Evaluate implement and deploy ML models to test and production vehicles
- Improve transition from research to production balancing cost
- Mentor engineers
- Optimize model training code for data formats
- Own ML infrastructure for training and deployment
- Own data management pipelines for ingestion storage and training
- Programmatically access cloud services
Perks/Benefits
- N/A
Skills/Tech-stack
Airflow | Apache Spark | Argo | Data Pipelines | Databricks | Distributed Systems | Infrastructure as Code | Jetson | Kubernetes | MLOps | Machine Learning | Model Monitoring | NodeJs | ONNX | Python | TensorRT | Terraform | Workflow Orchestration | “as-code”
Education
N/A
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