MLOps Architect / Engineer (0–12+ Years Experience)
Tasks
- Build automated ML pipelines
- Deploy ML workloads on Kubernetes
- Design enterprise mlops architecture
- Develop infrastructure as code for ml platforms
- Document platform architecture and runbooks
- Enforce ml platform governance and security
- Implement CI/CD for ML models
- Implement automated retraining
- Manage model versioning
- Monitor model drift and performance
- Set up monitoring logging observability
Perks/Benefits
Skills/Tech-stack
AI Pipelines | Amazon Web Services | Apache Airflow | Azure DevOps | Bash | CI/CD | Cloud platform | Drift Detection | Git | GitHub | GitHub Actions | GitLab | Google Cloud | Google Cloud Platform | Kubeflow | Kubernetes | MLOps | MLflow | Machine Learning | Microsoft Azure | Model Monitoring | Prometheus | Python | Sagemaker Pipelines | Terraform | Vertex AI | Vertex AI pipelines | Web Services
Education
Regions
Countries
States
Cities
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