Build and scale the infrastructure that powers AI at enterprise scale. Design robust, automated systems that enable data scientists and ML engineers to deploy, monitor, and maintain machine learning models in production environments.

Key Responsibilities:

• 
  Design and implement MLOps pipelines for model training, deployment, and monitoring
• 
  Build automated CI/CD systems for machine learning model lifecycle management
• 
  Develop infrastructure for real-time and batch ML inference at scale
• 
  Implement model monitoring, drift detection, and automated retraining systems
• 
  Design data pipelines and feature stores for ML model development and serving
• 
  Collaborate with data science teams to productionize research models
• 
  Optimize ML infrastructure for performance, cost, and reliability
• 
  Implement security and compliance controls for ML systems and data

Requirements

Benefits