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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 lifecycle-from ideation and PoC through production deployment and enablement-while 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.