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MLOps Engineer
4 weeks ago
Responsibilities :
- Pipeline Development : Design, build, and maintain CI/CD (Continuous Integration/Continuous Deployment) pipelines for automated model training, testing, and deployment.
- Automation : Automate workflows for model versioning, experimentation, and model retraining to ensure continuous improvement.
- Deployment and Integration : Deploy and integrate ML models into production environments, ensuring scalability and reliability.
- Monitoring and Management : Implement and manage monitoring tools to track model performance, system health, and resource utilization.
- Collaboration : Work closely with data scientists, data engineers, and software engineers to define requirements and integrate models into broader platforms.
- Troubleshooting : Identify and resolve issues in development, testing, and production environments related to models and the underlying infrastructure.
- Documentation : Maintain accurate and comprehensive documentation of MLOps processes, tools, and systems.
Requirements :
- Cloud Platforms : Experience with cloud providers such as AWS, Azure, and GCP is often required for building scalable cloud-native solutions.
- MLOps Platforms : Proficiency with MLOps platforms like MLflow, Kubeflow, DataRobot, or Dataiku.
- CI/CD Tools : Familiarity with CI/CD orchestration tools such as GitLab CI, GitHub Actions, and Airflow.
- Containerization : Expertise in containerization technologies like Docker.
- Programming Languages : Strong programming skills, often in Python, for scripting and automation.
- Monitoring Tools : Experience with monitoring tools like Prometheus and Grafana.
(ref:hirist.tech)
