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Machine Learning Operations Specialist
3 weeks ago
- Pipeline Development: Design, build, and manage scalable ML model deployment pipelines that meet business requirements.
- Model Automation: Automate model training, validation, monitoring, and retraining workflows using best practices.
- Governance and Versioning: Implement model governance, versioning, and reproducibility best practices to ensure data integrity.
- Collaboration: Collaborate with data scientists, engineers, and product teams to operationalize ML solutions that drive business outcomes.
- Performance Optimization: Ensure robust monitoring and performance tuning of deployed models to maximize efficiency.
- MLOps Tools: Strong experience with MLOps tools & frameworks (MLflow, Kubeflow, SageMaker, Vertex AI).
- Containerization: Proficient in containerization (Docker, Kubernetes) to ensure efficient model deployment.
- Cloud Platforms: Good knowledge of cloud platforms (AWS, Azure, or GCP) for scalability.
- Programming Languages: Expertise in Python and familiarity with ML libraries (TensorFlow, PyTorch, scikit-learn).
- Infrastructure as Code: Solid understanding of CI/CD, infrastructure as code, and automation tools to streamline operations.