Senior ML Ops Engineer (Machine Learning Infrastructure)
Tasks
- Architect and deploy scalable ML infrastructure for distributed training and inference
- Automate model evaluation, selection, and deployment workflows
- Build and operate cloud systems optimized for ML workloads in R&D and production
- Collaborate with ML engineers for data management and deployment requirements
- Design and implement MLOps pipelines for data management model training deployment monitoring
- Implement continuous integration and deployment for ML workflows experiment management governance
Perks/Benefits
Skills/Tech-stack
AWS | Amazon SageMaker | Apache Airflow | Azure | CI/CD | Cloud platform | Data Governance | Distributed Training | Experiment tracking | Git | Google Cloud | Google Cloud Platform | Inference | Kubeflow | MLOps | MLflow | Machine Learning | Metaflow | Model Deployment | Model Evaluation | Python | System design
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Regions
Countries
States
Cities
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