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.