AWS Sagemaker Developer

Experience: 7+ years in machine learning and data science. 3+ years developing and testing models with Amazon SageMaker. 3+ years deploying models using SageMaker endpoints and batch transforms. 2+ years implementing model monitoring with SageMaker Model Monitor. Key Responsibilities: Develop and test ML models using SageMaker (built-in algorithms/custom frameworks). Deploy models via endpoints, batch transforms, and multi-model configurations. Monitor model performance using SageMaker Model Monitor. Conduct A/B and shadow testing of new model versions. Optimize training performance using Spot Training and hyperparameter tuning. Collaborate with cross-functional teams to embed ML models in production applications. Required Skills: Proficiency in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn). Strong grasp of end-to-end ML workflows. Experience with AWS services (S3, CloudWatch, Lambda). Hands-on with SageMaker Studio and notebooks. Familiarity with MLOps and model governance practices. Preferred Qualifications: Experience with SageMaker Autopilot, Feature Store, and Pipelines. Knowledge of Docker/containerization. Background in distributed computing and large-scale data handling. Real-time and batch inference experience. AWS ML/Data Science certifications.