ML Infrastructure Engineer
Tasks
- Build model monitoring and MLOps workflows
- Deliver end to end ML infrastructure projects
- Design and deploy cloud and on prem ML training and evaluation infrastructure
- Develop cloud data infrastructure system
- Evaluate and implement model deployment methods for test and production vehicles
- Implement model conversion and optimization for production
- Improve transition of models from research to production with cost balance
- Own data management pipelines for ingestion storage and training
- Participate in model design and set deployment requirements
- Update model training code for improved data storage formats
Perks/Benefits
- N/A
Skills/Tech-stack
Amazon SageMaker | Apache Airflow | Apache Spark | Argo | Cloud platform | Data Engineering | Databricks | Distributed Systems | Google Cloud | Google Cloud Platform | Infrastructure as Code | Jetson | Kubernetes | MLOps | Machine Learning | Node.js | ONNX | Python | TensorRT | Terraform | Vertex AI | Workflow Orchestration | “as-code”
Education
N/A
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