ML Infrastructure Engineer
Tasks
- Build ML tooling and workflows
- Build simulation environments for ML validation
- Collaborate with data engineering and platform engineering
- Create end to end testing frameworks for ML
- Design ML deployment infrastructure
- Develop monitoring and observability systems
- Ensure reproducibility and safe rollout
- Implement automated rollback strategies
- Improve CI/CD pipelines for ML workloads
- Integrate ML systems with platform standards
- Maintain scalable ML model deployment pipelines
- Track model performance data drift and data quality
- Validate models and pipelines before deployment
Perks/Benefits
- Alternating day off
- Commuter allowance
- Company-paid training
- Health, dental, and vision
- Paid parental leave
Skills/Tech-stack
AWS | CI/CD | Data Drift | Data Quality | Feature Store | Kubernetes | MLOps | MLflow | Machine Learning | Monitoring | Observability | Python
Education
N/A
Regions
Countries
States
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