Related skills
azure docker terraform aws python📋 Description
- Design and maintain robust ML deployment pipelines to ensure seamless model delivery.
- Automate model training, deployment, and monitoring workflows.
- Collaborate with Data Scientists and Engineering teams to integrate models into production.
- Optimize cloud-based infrastructure for scalable and reliable ML systems.
- Implement CI/CD best practices for ML lifecycles.
- Monitor production systems and troubleshoot performance or governance issues.
🎯 Requirements
- Extensive experience as an MLOps or Machine Learning Engineer in production.
- Advanced proficiency in Python.
- Experience deploying and monitoring ML models at scale.
- Hands-on experience with Docker and Kubernetes.
- Cloud expertise (AWS, Azure, or GCP).
- Experience with CI/CD pipelines for ML workflows.
🎁 Benefits
- 100% remote work from anywhere with a reliable internet connection.
- Competitive USD compensation.
- Paid time off to rest and recharge.
- Autonomy to manage your time and deliver results.
- Work with leading U.S. companies on high-impact projects.
- Global network of 600+ professionals across 25+ countries.
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