ML Infrastructure Engineer
Los Angeles, California, United States
R
USD 145K-165K Mid-level Full Time
Tasks
- Automate ML lifecycle workflows for training validation and registry
- Build production model deployment and inference systems
- Create infrastructure as code for secure scalable environments
- Define ML infrastructure roadmap
- Design scalable ML development infrastructure
- Ensure governance reproducibility and auditability
- Establish model lifecycle management best practices
- Evaluate and implement model serving frameworks
- Experiment with tooling to improve reproducibility performance and developer velocity
- Implement CI CD workflows for ML automation
- Implement deployment and rollback strategies
- Implement monitoring for model performance latency drift and infrastructure health
- Manage training and inference workloads on AWS and GCP
- Support end to end ML workflows with data science and engineering teams
- Translate experimentation needs into production infrastructure solutions
Perks/Benefits
Skills/Tech-stack
AWS | Amazon SageMaker | BigQuery | BigQuery datasets | CI/CD | CloudFormation | CloudWatch | Datadog | Docker | Feature Engineering | Feature Stores | Flask | GCP | GPU Computing | Grafana | Kubernetes | Learning operations | MLflow | Machine Learning | Machine Learning Operations | Model Monitoring | Model Registry | Prometheus | Python | Terraform | Weights and Biases
Education
N/A
Regions
Countries
States
Cities
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