- 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).
- 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.