Description

You will design and implement cloud-based ML solutions and MLOps pipelines.

Responsibilities

• Build model and data pipelines for Data Scientists and Data Engineers using AWS services.
• Manage the full model lifecycle, including code refactoring, optimization, containerization, deployment, versioning, and quality monitoring.
• Prototype and evaluate new libraries and features to improve model development workflows.
• Collaborate with Data Science, Cloud Infrastructure, and development teams to define requirements and technical designs.
• Communicate complex technical information clearly to diverse stakeholders.

Required Skills

• 5+ years of experience in machine learning operations and cloud solutions.
• Proficiency in Python and standard Python ML libraries.
• Hands-on experience with AWS SageMaker, including SageMaker Studio, Jupyter Notebooks, Data Wrangler, and Clarify.
• Experience building data pipelines using S3, Lakeformation, SQS, SNS, Spark, Glue, Step Functions, and Lambda.
• Proficiency with IDE and notebook software such as Jupyter Studio, VSCode, or PyCharm.
• Experience with AWS data services.
• Ability to manage stakeholders and present technical designs effectively.

Preferred Skills

• Experience with data visualization tools such as Tableau or AWS QuickSight.

Key Skills

Education

Any Graduate