ML Ops and Model accuracy Engineer
Tasks
- Architect end-to-end MLOps pipelines
- Automate model lifecycle management
- Build cloud native infrastructure
- Design ML workload backend systems
- Enforce modular design automated testing and code quality
- Ensure high availability and performance
- Evaluate models with A B testing
- Implement CI/CD pipelines
- Implement automated retraining workflows
- Implement data ingestion deployment monitoring and reproducibility
- Manage Kubernetes containers and microservices
- Manage model versioning experiment tracking and governance
- Monitor model performance metrics
- Optimize for cost and resilience
- Provide logging metrics and tracing observability
Perks/Benefits
Skills/Tech-stack
AWS | Accuracy | Apache Airflow | Azure | Bias detection | CI/CD | Distributed tracing | Docker | Experiment tracking | GCP | Go | Infrastructure as Code | Java | Kubeflow | Kubernetes | Latency | Logging | MLOps | MLflow | Microservices | Model Drift | Model Governance | Model versioning | Monitoring | Python | Reproducibility | Terraform | “as-code”
Education
N/A
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