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

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