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.
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.