Related skills
azure docker aws python kubernetes📋 Description
- Design and maintain robust ML deployment pipelines.
- Automate model training, deployment, monitoring workflows.
- Collaborate with Data Scientists and Engineering teams to productionize models.
- Optimize cloud infrastructure for scalable ML systems.
- Implement CI/CD tailored for ML lifecycles.
- Monitor production ML systems; troubleshoot issues.
🎯 Requirements
- Extensive MLOps or ML Engineer experience in production.
- Advanced Python proficiency.
- Deploy ML models at scale; monitor/maintain.
- Docker and Kubernetes for containerization.
- Cloud platforms AWS, Azure, or GCP.
- CI/CD pipelines for ML workflows.
- Kubeflow, Airflow, or MLFlow exposure.
- Terraform IaC preferred.
🎁 Benefits
- 100% Remote Work.
- Competitive USD pay.
- Paid time off.
- Autonomy to manage your time and outcomes.
- Work with Top American Companies.
- Diverse, global network across LATAM.
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