MLOps Engineer - AWS/Azure

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