Aubum Hills, MI30+ days ago
Classical ML (regression, tree-based ensembles, gradient boosting, clustering) and deep learning (CNNs, RNNs, transformers) across supervised, unsupervised, and reinforcement paradigms; feature engineering, hyperparameter optimization, cross-validation, drift detection, and model evaluation; " end-to-end ML lifecycle on SageMaker spanning data preparation, training, deployment, monitoring, and retraining. " Define infrastructure patterns using Amazon EKS, AWS Lambda, ECS Fargate, API Gateway, EventBridge, SNS/SQS, Kinesis, S3, DynamoDB, Aurora, Redshift, Athena, OpenSearch, and Kendra.