Key Responsibilities

• Architect and maintain scalable ML platform components including feature stores and model registries
• Develop automated pipelines to combat data drift and model degradation
• Design high-scale feature engineering systems for identity resolution and predictive modeling
• Optimize model inference latency to meet enterprise-level Service Level Agreements
• Set the technical direction and MLOps standards for the entire ML engineering organization

Essential

• Extensive experience building and scaling infrastructure for diverse machine learning models
• Proven ability to lead complex ML projects across multiple engineering teams
• Expertise in end-to-end ML system ownership from development to production
• Proficiency in architecting MLOps components like feature stores and model registries
• Experience designing automated pipelines for model training and deployment
• Ability to manage infrastructure costs while meeting strict inference latency SLAs

Preferred

• Experience with identity resolution or customer segmentation models
• Familiarity with AI-assisted development tools such as Claude Code
• Background in building real-time and batch feature engineering systems
• Experience establishing organizational SLOs and operational standards for ML

Key Skills