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Building Scalable AI Infrastructure
3 weeks ago
- Design and implement scalable and efficient infrastructure to support machine learning model development and deployment.
- Develop and maintain scripts and tools to automate various tasks such as data preprocessing, feature engineering, and model training.
- Collaborate with cross-functional teams to integrate mlflow, kubeflow, and grafana into existing workflows.
- Implement monitoring and logging systems using prometheus and grafana to ensure timely detection and resolution of issues.
- Stay up-to-date with industry trends and emerging technologies in machine learning and artificial intelligence.
- Strong background in machine learning, deep learning, and software development.
- Experience with programming languages such as Python, Java, or C++.
- Familiarity with popular machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Knowledge of containerization using Docker and orchestration using Kubernetes.
- Experience with continuous integration and continuous deployment (CI/CD) pipelines.
- A comprehensive health insurance plan.
- A generous retirement savings plan.
- A flexible work-from-home policy.
- A professional development budget.
