Description

You will own the refactoring, tuning, and optimization of ML solutions within the recommendations domain platform.

This role is on-site.

Responsibilities

• Refactor, tune, and optimize ML solutions to ensure production readiness.
• Containerize and productionalize models using AWS SageMaker and other platform tools.
• Build and automate testing frameworks for ML model validation and testing.
• Collaborate with Consumer Data Sciences, MLOps Core, and Platform teams to deliver end-to-end production models.
• Lead multiple squads to execute delivery objectives.

Required Skills

• 5+ years of experience in AI/ML engineering.
• Proficiency in Python and PySpark.
• Hands-on experience with Databricks.
• Experience deploying and managing models via AWS SageMaker.
• Strong understanding of MLOps principles and AWS infrastructure.
• Experience with containerization for model deployment.
• Ability to design automated testing and validation frameworks for machine learning.
• Proven leadership capability managing multiple engineering squads.

Preferred Skills

• Any Graduate degree.

Key Skills

Education

Any Graduate