Ontology Knowledge Architect

Lam Research•Fremont, CA

1d•$166,000 - $350,000•Hybrid

About The Position

Enterprise AI is building the foundation for Lam’s AI-first future. We partner across business, data, engineering, security, and technology teams to create scalable AI platforms, governed agent architectures, AI-ready knowledge assets, and reusable solution patterns that help Lam accelerate innovation, improve operations, and turn proprietary domain expertise into durable enterprise advantage. As an AI Ontology Knowledge Architect, you will define and help build how Lam represents, governs, connects, and operationalizes enterprise meaning for AI. This is a hands-on architecture role at the intersection of ontology architecture, semantic modeling, data pipelines, knowledge engineering, agentic AI, and domain understanding. You will not only create reference architectures and standards—you will be directly involved in making them real. You will work with engineers, domain experts, platform teams, and AI solution teams to turn architectural patterns into working ontology models, knowledge assets, integration pipelines, MCP-enabled tools, and agent-ready semantic services. Your work will help ensure that AI systems, copilots, and agents understand business context, reason over trusted knowledge, operate within governed boundaries, and enable decisions and actions that are explainable, reusable, and scalable.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Science, Engineering, Mathematics, or a related technical field; Master’s or PhD preferred.
12+ years of experience in enterprise architecture, data architecture, knowledge architecture, semantic modeling, AI architecture, or related technical leadership roles.
Hands-on experience designing and implementing enterprise-scale data, semantic, ontology, or knowledge graph architectures across complex business domains.
Strong understanding of data pipelines, data integration, metadata, lineage, governance, access control, data quality, and data product design.
Experience with ontologies, taxonomies, semantic models, graph models, knowledge graphs, business object models, or domain-driven design.
Familiarity with modern cloud data and AI ecosystems such as Microsoft Fabric, Azure, Databricks, Synapse, AI Foundry, graph databases, vector stores, search platforms, or comparable technologies.
Experience building AI-ready knowledge patterns for LLMs, copilots, retrieval-augmented generation, agent orchestration, semantic search, or decision-support applications.
Understanding of MCP, API-based tool integration, agent-tool interaction patterns, and security/governance considerations for AI systems that access enterprise tools and data.
Ability to move fluidly between strategy and execution—creating architecture direction while also working directly with teams to implement, validate, and refine it.
Strong communication skills, including the ability to influence senior leaders and create clear architecture artifacts, standards, and roadmaps.

Nice To Haves

Experience with ontology or operational AI platforms such as Palantir Foundry / AIP, Microsoft Fabric / Fabric IQ, or comparable enterprise semantic and AI platforms.
Experience with graph technologies, semantic standards, metadata platforms, catalogs, data governance platforms, or ontology engineering tools.
Familiarity with enterprise manufacturing, semiconductor, supply chain, engineering, product lifecycle, quality, finance, or operations domains.
Experience designing and delivering architectures for agentic AI systems, including grounded context, memory, tool use, action governance, feedback loops, and human-in-the-loop controls.
Strong understanding of security, IAM, least privilege, auditability, and responsible AI principles for enterprise AI systems.
Ability to balance near-term delivery with long-term architecture direction in a rapidly evolving AI landscape.

Responsibilities

Define and evolve Lam’s enterprise ontology and semantic architecture across business objects, relationships, decisions, actions, permissions, events, and AI-ready knowledge assets.
Hands-on design and implementation of reusable patterns that connect source systems, data pipelines, metadata, semantic models, knowledge graphs, AI Search, vector stores, MCP tools, and agent orchestration frameworks.
Partner with business domain experts to model enterprise domains, processes, decision flows, operational states, and domain-specific knowledge for use by agents, copilots, applications, analytics, and automation.
Translate architecture into working implementations, prototypes, platform patterns, graph structures, semantic services, and integration designs that delivery teams can adopt and scale.
Create standards for ontology-data integration, including schema mapping, master-data alignment, graph design, metadata enrichment, lineage, observability, versioning, and access control.
Define interoperability patterns across ontology platforms and AI ecosystems, including Palantir, Microsoft Fabric / Fabric IQ, Azure AI services, internal data platforms, and enterprise applications.
Establish architecture for AI-ready knowledge assets, including structured and unstructured knowledge, semantic analysis, knowledge extraction, embeddings, retrieval, and feedback loops.
Design how MCP servers, APIs, tools, data services, and agent actions should be represented, secured, governed, and connected into ontology-aware AI workflows.
Collaborate with AI solution architects, data architects, platform engineers, security, IAM, governance, and domain teams to ensure agents operate with grounded context, trusted actions, and appropriate human oversight.
Evaluate emerging ontology and knowledge platform capabilities and recommend patterns that balance speed, interoperability, portability, cost, governance, and long-term enterprise differentiation.
Build reference architectures, standards, decision frameworks, implementation patterns, and governance models for ontology and knowledge lifecycle management.