AI Engineer / Machine Learning Engineer – MLOps
Tasks
- Automate workflows with CI CD for ML models
- Build ML pipelines for training testing and deployment
- Collaborate to productionize models
- Containerize applications
- Deploy machine learning and AI models to production
- Deploy models on cloud platforms
- Ensure scalability, security, and reliability
- Implement monitoring and logging for ML systems
- Manage model lifecycle including monitoring and retraining
- Manage model versioning data versioning and experiment tracking
- Monitor model performance drift and data quality
Perks/Benefits
Skills/Tech-stack
AWS | Amazon SageMaker | Apache Airflow | Azure Machine Learning | CI/CD | Cloud platform | DVC | Data Pipelines | Docker | FastAPI | Flask | GitHub Actions | GitLab CI | Google Cloud | Google Cloud Platform | Google Vertex | Google Vertex AI | Jenkins | Kubeflow | Kubernetes | MLOps | MLflow | Machine Learning | Microsoft Azure | Model Logging | Model Monitoring | NoSQL | Python | SQL | Terraform | Vertex AI | Weights and Biases
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | MLOps Engineer | Machine Learning Engineer
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