Related skills
docker aws python kubernetes airflow📋 Description
- Design and maintain robust ML deployment pipelines for seamless model delivery.
- Automate training, deployment, and monitoring of ML workflows.
- Collaborate with Data Scientists and Engineering to productionize models.
- Optimize cloud infrastructure for scalable, reliable ML systems.
- Implement CI/CD practices for ML lifecycles.
- Monitor production systems and troubleshoot performance/governance issues.
🎯 Requirements
- Extensive MLOps/ML Engineer experience in production environments.
- Advanced Python proficiency.
- Proven track record deploying ML models at scale.
- Hands-on Docker and Kubernetes experience.
- Cloud platforms: AWS, Azure, or GCP.
- CI/CD pipelines for ML workflows.
- Kubeflow, Airflow, or MLFlow orchestration tools (preferred).
🎁 Benefits
- 100% Remote Work: Work from anywhere you thrive.
- Highly Competitive USD Pay: USD compensation.
- Paid Time Off: Well-being and recharge.
- Work with Autonomy: Manage your time and outcomes.
- Work with Top American Companies: High-impact projects.
- Diverse, Global Network: 25+ countries.
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