• Model Development & Training: Building and refining ML models using frameworks like TensorFlow, PyTorch, and Scikit-learn within SageMaker Studio.
• Data Engineering & Labeling: Designing automated data pipelines and managing high-quality datasets using tools like SageMaker Ground Truth and SageMaker Data Wrangler.
• Operationalizing ML (MLOps): Implementing CI/CD for machine learning through SageMaker Pipelines, automating model retraining, and managing model versions in the SageMaker Model Registry.
• Deployment & Inference: Deploying models for real-time or batch inference and managing multi-model endpoints to ensure low latency and high availability.
• Performance Monitoring: Using SageMaker Model Monitor and Clarify to track model quality, detect bias, and identify feature drift in production.
• Optimization: Tuning hyperparameters and optimizing training costs using Managed Spot Training and distributed training libraries.

Essential Skills & Qualifications

• AWS Expertise: Proficiency in Amazon SageMaker and related services such as S3, Lambda, IAM, and Step Functions.
• Programming: Strong command of Python (specifically the SageMaker Python SDK) or R, and SQL.
• ML Frameworks: Deep experience with modern libraries including PyTorch, TensorFlow, and XGBoost.
• Mathematical Foundation: Solid understanding of statistics, linear algebra, and predictive modeling.
• Cloud Infrastructure: Experience managing compute clusters, VPCs, and ensuring security best practices.