Related skills
azure docker aws python kubernetes📋 Description
- Design and maintain robust ML deployment pipelines to ensure seamless model delivery.
- Automate model training, deployment, and monitoring workflows to improve efficiency.
- Collaborate with Data Scientists and Engineers to productionize models.
- Optimize cloud infrastructure for scalable, reliable ML systems.
- Implement CI/CD practices tailored for ML lifecycles.
- Monitor production systems and troubleshoot performance or governance issues.
🎯 Requirements
- Extensive experience as an MLOps Engineer or Machine Learning Engineer in production.
- Advanced proficiency in Python.
- Hands-on with Docker and Kubernetes for containerization and orchestration.
- Cloud platforms AWS, Azure, or GCP.
- CI/CD pipelines for ML workflows.
- Kubeflow, Airflow, or MLFlow for ML orchestration.
- Terraform for Infrastructure as Code (IaC).
- Familiarity with Databricks, SageMaker, or Vertex AI.
🎁 Benefits
- 100% Remote Work: Freedom to work from anywhere.
- Paid Time Off: Well-being and recharge.
- Work with Autonomy: Manage your time and results.
- Work with Top American Companies: High-impact projects.
- Diverse, Global Network: 600+ professionals in 25+ countries.
- Culture that values well-being and work-life balance.
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