Principal Practice Enterprise Architect AI

Sherwin-Williams•Cleveland, OH

12h•Remote

About The Position

The Principal Practice Architect, AI is the enterprise architecture decision maker for artificial intelligence. The role owns the standards, reference architecture, guardrails, technology selections, and roadmap that portfolios follow when building with AI. Portfolio Architects design within the established standards, Solution and Domain Architects conform to them, and platform teams implement them. The architect works as a peer with senior IT, data, security, and risk leaders. The role defines enterprise agentic strategy and AI architecture patterns; selects AI technologies and platforms; establishes Security and Responsible AI guardrails; and sets the enterprise AI platform strategy and roadmap. It reviews AI solution designs for conformance, aligns the AI Center of Excellence’s platform delivery with enterprise standards, defines AI governance and operating models, and guides enterprise AI initiatives. The position also tracks the evolving AI technology landscape, publishes best practices, builds architecture capability through enablement and hands-on guidance, and contributes AI expertise to architecture governance, enterprise strategy, and investment decisions. This is a remote position. Travel is 10–15%. The role is not eligible for work-authorization sponsorship now or in the future. Employment requires being at least 18 years old and legally authorized to work in the country of employment without current or future sponsorship. The company states that a criminal-history review is necessary because the job involves contact with employees and access to confidential or proprietary information and other items of value.

Requirements

Must be at least 18 years of age.
Must be legally authorized to work in the country of employment without now or future sponsorship for employment visa status.
Bachelor’s degree in Computer Science or Information Technology, or at least 18 years of technology-delivery experience in lieu of a degree.
15 or more years in technology, including 5 or more years of hands-on software or machine-learning engineering and 8 or more years in architecture or deep technical leadership roles.
Production experience designing and governing enterprise-scale AI systems, including LLM applications, agent frameworks, model orchestration, retrieval and grounding, evaluation harnesses, and guardrails; advisory-only AI experience does not qualify.
3 or more years setting enterprise technology standards and making platform-selection decisions across multiple business units or delivery teams.
Working depth in the ML lifecycle, MLOps, data architecture, and integration patterns sufficient to design AI workloads that reuse the existing foundation.
Current hands-on use of AI code-assist and agentic development tooling; ability to evaluate AI platforms directly rather than through vendor material.
Track record of influencing senior technical leaders as a peer and enforcing standards across delivery teams without creating bottlenecks.

Nice To Haves

TOGAF Level 2.
CISSP.
CEAP.
CSA.
CSEA.

Responsibilities

Define enterprise agentic strategy and own AI standards, patterns, and reference architectures covering agentic patterns, model orchestration and gateways, retrieval and grounding, evaluation harnesses, and use of shared data and integration foundations.
Own enterprise AI technology and platform selection, including model providers, agent frameworks, orchestration, evaluation and observability tools, and AI code-assist tools, in consultation with the Data, Integration, and Infrastructure Practice Architects.
Own Security and Responsible AI guardrails with Security, Legal, and Risk, including model risk classification, evaluation criteria, human-in-the-loop requirements, data protection for AI workloads, and conditions for production release.
Own the enterprise AI platform strategy and roadmap, including build-versus-buy decisions, platform consolidation, capability expansion, version currency, and sunset planning as input to investment decisions.
Review AI solution designs for conformance to enterprise standards and advise Portfolio Architects where their decisions intersect AI without overriding them.
Keep the AI Center of Excellence’s platform delivery aligned with the enterprise standard and provide architecture direction for Enterprise AI Game Changers initiatives.
Define the AI governance and operating model for proposing, approving, building, and monitoring use cases, including division of work among the AI Center of Excellence, portfolios, and architecture.
Run the AI technology radar, assessing emerging, maturing, and declining technologies at a cadence suited to the changing market.
Document and publish AI best practices, tooling, design patterns, and LLM guidance; develop AI architecture capability among Solution and Domain Architects through enablement and hands-on guidance.
Serve as a standing member of architecture governance and contribute AI expertise to enterprise strategy and investment decisions.