Senior Director, Enterprise AI & Automation - Santa Clara, CA, United States

Originally posted 11 June 2026 by the employer.

About the role

The Senior Director, Enterprise AI & Automation drives the enterprise-wide strategy and execution of AI, Generative AI, Agentic Automation, MLOps, and Robotic Process Automation (RPA) capabilities, reporting to the VP of Engineering.

What you'll do

- Own the enterprise AI and GenAI strategy and PoC-to-production lifecycle across domains including Contract Analytics, Quality, Finance, and Supply Chain. - Architect and govern enterprise Agentic AI systems and multi-agent frameworks, integrating LLM-powered tools with ERP, CRM, and ITSM platforms. - Enable secure enterprise access to LLMs across Azure AI Foundry, AWS Bedrock, and GCP Vertex AI while supporting MCP (Model Context Protocol) servers. - Lead end-to-end MLOps programs covering training pipelines, CI/CD for ML, feature stores, drift monitoring, and ML infrastructure modernization using Databricks. - Direct the RPA Center of Excellence and enterprise automation programs utilizing platforms such as UiPath AutoPilot.

What you'll need

- 15+ years of experience in software engineering, data science, or AI/ML, with 7+ years leading large engineering or AI teams. - Proven track record delivering enterprise-scale AI/GenAI programs and building production MLOps platforms. - Deep technical expertise with LLMs, including fine-tuning, prompt engineering, RAG architectures, and domain adaptation. - Practical experience with multi-cloud AI platforms (Azure AI Foundry, AWS Bedrock, GCP Vertex AI) and MLOps tooling such as Databricks/MLflow, Kubeflow, or SageMaker. - Experience deploying RPA platforms (UiPath, Blue Prism, or Automation Anywhere) and agentic AI frameworks such as LangChain, AutoGen, or CrewAI. - Python fluency with AI/ML libraries including PyTorch and Hugging Face Transformers, alongside experience with Model Context Protocol (MCP) and agent orchestration patterns.

Nice to have

- Experience applying AI/ML to quality, supply chain, or engineering operations within semiconductor, advanced manufacturing, or capital equipment industries. - Contributions to enterprise AI governance frameworks, responsible AI policies, or recognized industry contributions (publications, talks, open-source). - Degree in Computer Science, Machine Learning, or a related technical field.

Skills: AI, Generative AI, MLOps, LLMs, RPA, multi-cloud AI platforms