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 recent 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).
Robust 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.