Senior Director, Enterprise AI & Automation

Position Overview

Applied Materials is seeking a visionary engineering leader to drive 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 this leader will own the full AI/GenAI lifecyclefrom ideation and PoC through production deployment and enablementwhile leading cross-functional teams influencing senior stakeholders and building a culture of innovation and responsible AI use.

Key Responsibilities

Strategic AI & GenAI Leadership

• Own the Enterprise AI/GenAI strategy and PoC-to-production delivery across domains (Contract Analytics Quality Finance Supply Chain) aligning investments with business priorities.

• Architect and govern the Enterprise Agentic AI Strategy deploying multi-agent frameworks and LLM-powered tools at scale.

• Drive fine-tuning prompt engineering and domain adaptation of LLMs for Applied Materials use cases; contribute to AI governance and commercialization at the executive level and represent the organization at industry conferences (e.g. ET India EPTC Singapore).

Enterprise LLM Platform & Multi-Cloud AI Infrastructure

• Enable secure enterprise access to 100 LLMs across Azure AI Foundry AWS Bedrock and GCP Vertex AI integrating open-source models via artifact platforms (e.g. JFrog).

• Partner with Security Legal and Infrastructure to streamline PoC cycles standardize sizing and ensure compliant AI deployment.

• Enable autonomous AI environments and MCP (Model Context Protocol) servers for internal tools and agentic workflows.

MLOps & Model Lifecycle Management

• Lead end-to-end MLOps programs training pipelines CI/CD for ML feature stores and production monitoring and drive modernization of ML infrastructure (e.g. CDSW Databricks).

• Establish model governance (bias detection explainability drift monitoring retraining) and champion Databricks best practices across data science and engineering teams.

Agentic AI & Automation

• Architect and deploy enterprise-grade multi-agent AI systems for complex multi-step workflows integrating with ERP CRM and ITSM to automate high-value decisions end-to-end.

• Design internal AI tools and agents (e.g. AI Finance Bot domain-specific LLM applications) and lead the roadmap for next-generation agentic platforms as emerging capabilities mature.

Robotic Process Automation (RPA)

• Lead the RPA Center of Excellence and enterprise-scale automation programs delivering measurable cost avoidance ($100M annually) and operational efficiency gains.

• Champion modern RPA platforms (UiPath AutoPilot) and AI-augmented tooling; expand automated ticket resolution to 60% of support requests.

• Establish RPA governance change control and operational KPIs to sustain reliability and scalability of the automation estate.

Required Qualifications

Experience

• 15 years in software engineering data science or AI/ML with 7 years leading large engineering or AI teams.

• Track record delivering enterprise-scale AI/GenAI programs with measurable business impact and building/scaling production MLOps platforms.

• Experience deploying RPA programs at scale (UiPath Blue Prism Automation Anywhere or equivalent) and hands-on with agentic AI frameworks (LangChain AutoGen CrewAI or comparable).

• Experience leading governance legal and security review processes for AI/GenAI deployments in regulated or enterprise contexts.

Technical Skills

• Deep LLM expertise: fine-tuning prompt engineering RAG architectures and domain adaptation.

• Multi-cloud AI platforms (Azure AI Foundry AWS Bedrock GCP Vertex AI) and MLOps tooling (Databricks/MLflow Kubeflow SageMaker or equivalent) with model monitoring and drift detection.

• RPA development and platform management: UiPath (including AutoPilot) with automation governance and COE operations.

• Python fluency with modern AI/ML libraries (PyTorch Hugging Face Transformers LangChain).

• MCP (Model Context Protocol) tool-calling and agent orchestration patterns; enterprise integration API-based automation and workflow orchestration.

Preferred Qualifications

• Experience in semiconductor advanced manufacturing or capital equipment industries with familiarity applying AI/ML to quality supply chain or engineering operations.

• Contributions to AI governance frameworks responsible AI policies or AI ethics programs at the enterprise level plus recognized industry contributions (awards publications conference talks open-source).

• Degree in Computer Science Machine Learning or a related technical field.