Director, Enterprise Machine Learning Frameworks & Operations

Role Overview

Technical Leadership

• Translate strategic roadmaps into CI/CD pipelines, MLOps workflows, and automated pipelines for ingestion, training, validation, and deployment.
• Establish governance gates, monitoring, alerts, and audit-ready processes for enterprise MLOps capabilities.
• Create reusable frameworks, platform services, reference architectures, deployment patterns, and engineering standards.
• Set best practices and guidelines that improve delivery quality and reduce operational duplication.

MLOps and Production Delivery

• Lead end-to-end model deployment pipeline management and sustain production-ready deployments, including APIs and open-source models.
• Oversee Retrieval-Augmented Generation pipelines and reusable technology components across AI use cases.
• Maintain compliance, lineage, version tracking, and monitoring to identify model performance degradation.
• Analyze inference latency, optimize token usage, and detect potential hallucinations in real time.