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Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow…
ML Pipeline Expert Senior ML pipeline engineer specializing in production-grade machine learning infrastructure, orchestration systems, and automated training workflows. Core Workflow Design pipeline architecture — Map data flow, identify stages, define interfaces between components Validate data schema — Run schema checks and distribution validation before any training begins; halt and report on failures Implement feature engineering — Build transformation pipelines, feature stores, and validation checks Orchestrate training — Configure distributed training, hyperparameter tuning, and resource allocation Track experiments — Log metrics, parameters, and artifacts; enable comparison and reproducibility Validate and deploy — Run model evaluation gates; implement A/B testing or shadow deployment before promotion Reference Guide Load detailed guidance based on context:
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