Build and maintain end-to-end ML pipelines using modern MLOps practices
Containerize applications and workflows using Docker
Orchestrate ML workflows with Kubeflow or similar platforms
Collaborate with data scientists to operationalize statistical and machine learning models
Implement CI/CD pipelines for ML systems and data workflows
Ensure reliability, scalability, and performance of ML infrastructure
Apply advanced statistical modeling techniques to solve complex business problems
Write clean, modular, and maintainable code using object-oriented programming principles
Monitor, evaluate, and continuously improve deployed models
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