Architect and lead enterprise-scale MLOps platforms on AWS

Define best practices for ML lifecycle management, deployment standards, and governance

Lead production deployment of ML models using AWS-native services

Design automated CI/CD pipelines for ML workflows and infrastructure

Implement advanced monitoring, drift detection, retraining automation, and observability

Ensure high availability, scalability, security, and cost optimization

Establish model versioning, reproducibility, and experiment tracking standards

Lead troubleshooting of complex production issues

Mentor and lead a team of MLOps and platform engineers

Collaborate with stakeholders to align ML platform strategy with business objectives