AI Platform Lead: Agentic & Enterprise AI Architect

Applied Materials, Inc.; Austin (TX); On-site; USD 176,000 - 242,000; Full time

Job summary

Applied Materials, Inc. is seeking a leader to architect and deliver enterprise AI platforms, leveraging Azure AI, Databricks, and multi-agent systems.

You will mentor teams, drive production-grade AI solutions, and partner with product, engineering, and data science teams to unlock autonomous decision-making across manufacturing and operations. The role emphasizes hands-on coding, architecture reviews, and governance to ensure scalable, observable AI deployments that deliver measurable business

Qualifications

• Recognized technical expert in Generative AI, Agentic AI, Multi-Agent Architectures, and Enterprise AI Platforms.
• Deep expertise in LLMs, RAG, vector databases, knowledge graphs, AI orchestration frameworks, machine learning, and cloud-native architectures.
• Strong hands-on software engineering with Python and modern AI development frameworks.

Responsibilities

• Lead the architecture, design, and implementation of Agentic AI solutions and Multi-Agent Systems that solve complex business and manufacturing challenges through autonomous reasoning, planning, orchestration, and execution.
• Drive the development of AI agents using modern frameworks (e.g., LangGraph, AutoGen, CrewAI, Semantic Kernel, OpenAI Agents, Azure AI Foundry) to enable decision intelligence, workflow automation, knowledge retrieval, and operational optimization.
• Serve as a hands-on technical leader responsible for building scalable AI platforms, including agent orchestration, memory management, tool integration, Retrieval-Augmented Generation (RAG), knowledge graphs, ontologies, and enterprise AI architectures.
• Lead the development of advanced AI capabilities, including reasoning agents, planning agents, orchestration agents, code-generation agents, analytics agents, and domain-specific copilots that improve business outcomes and operational efficiency.
• Collaborate with business stakeholders, product teams, engineers, data scientists, and subject matter experts to identify high-value AI use cases and translate them into production-grade AI solutions.
• Establish AI engineering best practices covering LLMOps, AI governance, evaluation frameworks, observability, security, safety, prompt engineering, context engineering, model optimization, and continuous improvement.
• Architect and develop enterprise AI platforms leveraging Azure AI, Databricks, Python, vector databases, graph databases, cloud-native technologies, and modern machine learning frameworks.
• Build and optimize agent memory architectures, semantic layers, knowledge repositories, and enterprise ontologies to improve reasoning quality, contextual awareness, and autonomous execution.
• Lead proof-of-concept development, rapid prototyping, and production deployments while ensuring scalability, reliability, maintainability, and measurable business value.
• Mentor and guide AI engineers and data scientists while remaining actively involved in coding, architecture reviews, solution design, model development, and technical problem solving.
• Stay current with emerging advances in Generative AI, Agentic AI, foundation models, reasoning systems, and autonomous agents, driving adoption of innovative technologies across the organization.

Skills

Generative AI

Agentic AI

Multi-Agent Architectures

Enterprise AI Platforms

LLMs

RAG

Knowledge graphs

AI governance

Education

Bachelor's degree in CS/AI/DS

Tools

Azure AI

Databricks

OpenAI

LangGraph

Semantic Kernel

CrewAI

AutoGen

Vector databases

Job description

Applied Materials, Inc. is seeking a leader to architect and deliver enterprise AI platforms, leveraging Azure AI, Databricks, and multi-agent systems.

You will mentor teams, drive production-grade AI solutions, and partner with product, engineering, and data science teams to unlock autonomous decision-making across manufacturing and operations. The role emphasizes hands-on coding, architecture reviews, and governance to ensure scalable, observable AI deployments that deliver measurable business