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Applied Materials

Builds equipment for chip and display factories

Manager Data Scientist

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeAustin, TX, USA

About the Job

Requirements

  • A bachelor's degree in Computer Science, Artificial Intelligence, or Data Science is required.
  • Recognized technical expertise in Generative AI, Agentic AI, Multi-Agent Architectures, and Enterprise AI Platforms.
  • Deep expertise in Large Language Models, Retrieval-Augmented Generation, vector databases, knowledge graphs, AI orchestration frameworks, machine learning, and cloud-native architectures.
  • Strong hands-on software engineering capabilities with Python and modern AI development frameworks.
  • Demonstrated ability to design scalable, production-ready AI systems across multiple technology domains.
  • Anticipates emerging AI technology trends and identifies opportunities to create competitive advantages through AI-driven automation and intelligence.
  • Partners with business leaders to define AI strategy, prioritize use cases, and deliver measurable business outcomes through autonomous and intelligent systems.
  • Understands manufacturing, supply chain, engineering, operational, and enterprise business processes and how Agentic AI can transform them.
  • Communicates complex AI concepts and architectures effectively to executive leadership, technical teams, and business stakeholders.
  • Drives alignment across diverse organizations and builds consensus around AI strategy and solution approaches.
  • Seven or more years of software engineering, data science, machine learning, or AI experience is preferred.
  • Three or more years building production-grade AI/ML solutions using Python is preferred.
  • Three or more years developing Generative AI, Agentic AI, Multi-Agent Systems, Retrieval-Augmented Generation, knowledge graphs, or LLM applications is preferred.
  • Experience with Azure AI, Databricks, OpenAI, LangGraph, Semantic Kernel, CrewAI, AutoGen, vector databases, and cloud-native architectures is preferred.
  • A demonstrated track record delivering enterprise-scale AI products from concept through production is preferred.

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 such as LangGraph, AutoGen, CrewAI, Semantic Kernel, OpenAI Agents, and Azure AI Foundry to enable decision intelligence, workflow automation, knowledge retrieval, and operational optimization.
  • Build scalable AI platforms, including agent orchestration, memory management, tool integration, Retrieval-Augmented Generation, knowledge graphs, ontologies, and enterprise AI architectures.
  • Lead development of 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 advances in Generative AI, Agentic AI, foundation models, reasoning systems, and autonomous agents, and drive adoption of innovative technologies across the organization.
  • Lead complex AI transformation initiatives from strategy through implementation and production deployment.
  • Drive cross-functional teams delivering enterprise-scale Agentic AI and automation solutions.
  • Influence technical direction, architecture standards, and AI governance across the organization.
  • Solve complex and ambiguous business and technical problems through innovative application of AI, machine learning, and autonomous agent technologies.
  • Develop approaches for reasoning, planning, orchestration, workflow automation, and knowledge-driven decision making.
  • Balance experimentation and innovation with production-grade engineering principles.
  • Influence enterprise AI strategy, technology investments, architecture decisions, and adoption of next-generation AI capabilities.
  • Deliver scalable AI solutions that improve productivity, operational performance, decision quality, and business agility.
  • Shape long-term AI platform roadmaps and standards for the organization.
  • Promote adoption of AI best practices across the enterprise.

About the Company

Applied Materials

Builds equipment for chip and display factories

Applied Materials designs and builds precision machines that chipmakers and display manufacturers use to form, shape and inspect thin layers of material. Its platforms handle steps such as depositing films and removing unwanted material, helping chipmakers create transistors and wiring at atomic scale. Customers include makers of logic chips and memory, as well as manufacturers of advanced displays. The company earns money from equipment sales, replacement parts and services, and factory software that schedules production and monitors manufacturing processes. Applied Materials was founded in 1967 and is headquartered in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1967

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