MLOps Engineer - AWS/Azure

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.