Description

You will design, build, and deploy scalable ML models on AWS SageMaker, ensuring performance and reliability.

Responsibilities

• Design, build, and deploy scalable ML models on AWS SageMaker.
• Automate model training and deployment pipelines using SageMaker Pipelines and MLOps practices.
• Implement feature engineering, hyperparameter tuning, and model optimization within the SageMaker ecosystem.
• Leverage AWS Glue for ETL processes to prepare data for ML workflows.
• Monitor model performance and detect anomalies using CloudWatch.

Required Skills

• 7 to 10 years of experience in machine learning, model development, and deployment.
• Strong expertise in AWS SageMaker (training, inference, pipelines, tuning).
• Proficiency in Python and PySpark for large-scale datasets.
• Hands-on experience with AWS Glue for data transformation.
• Experience with CloudWatch for system monitoring.
• Strong understanding of MLOps best practices.
• Excellent written and verbal communication for client interaction.
• Familiarity with SQL and NoSQL databases.

Key Skills

Education

Any Gradute