Senior ML Ops Engineer (Machine Learning Infrastructure)
Tasks
- Architect deploy manage scalable ML infrastructure for distributed training and inference
- Automate model evaluation, selection, and deployment workflows
- Build and operate cloud based systems for ML workloads in R and production
- Collaborate with ML engineers to gather requirements and plan data management and deployment
- Design MLOps automation pipelines for data management model training deployment monitoring
- Implement continuous integration and deployment for ML infrastructure experiment management governance
Perks/Benefits
Skills/Tech-stack
AWS | Amazon SageMaker | Apache Airflow | CI/CD | Data Ingestion | Distributed Training | Docker | Experiment tracking | GCP | Git | Kubeflow | Kubernetes | MLOps | MLflow | Machine Learning | Metaflow | Model Deployment | Model Evaluation | Model Monitoring | Python
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Regions
Countries
States
Cities
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