Tech Stack

Description

You will design and build scalable data pipelines and feature infrastructure that feed foundation models, and contribute to the ML lifecycle from data ingestion to model deployment and monitoring. You'll partner with product, compliance, and ML teams to ensure models are auditable and deliver measurable business value.

Requirements

• 6+ years experience in ML engineering or data engineering
• Proven experience building data ingestion pipelines at scale with distributed computing (Ray, Spark)
• Strong background in applied ML, including model training and hyperparameter tuning
• Proficiency in Python for data engineering and ML workflows
• Solid understanding of data quality principles and monitoring systems

Responsibilities

• Design and build scalable data ingestion pipelines for AI Core platform
• Implement data quality monitoring and validation systems
• Model new types of data into foundation models, analyzing impact on performance
• Develop and maintain data preparation workflows using distributed computing frameworks like Ray
• Tune and optimize ML models when new datasets are integrated