Principal Practice Enterprise Architect AI

The Sherwin-Williams Company; Cleveland (OH); Remote; USD 190,000 - 260,000; Full time

Job summary

The Sherwin-Williams Company seeks a Principal Practice Architect, AI to establish enterprise AI standards, reference architectures, and guardrails. You will drive technology selection, governance and roadmaps across portfolios, aligning Solution & Domain Architects and platform teams under a unified AI strategy.

You will work with senior IT, data, security and risk leaders to set the standard for AI workloads and ensure compliant, scalable deployments, including governance reviews and

Qualifications

• Requires 18+ years of age and work authorization in the United States.
• Bachelor's degree in CS/IT or equivalent experience.
• 15+ years in technology with 5+ years in hands-on software or ML engineering and 8+ years in architecture/leadership.
• Production experience designing and governing enterprise-scale AI systems (LLM apps, agent frameworks, governance).

Responsibilities

• Define enterprise AI standards, patterns and reference architectures.
• Own AI platform selection, governance and tooling.
• Defining AI governance and operating model across portfolios and architecture groups.
• Review designs for conformance to AI standards without overriding decisions.
• Publish AI best practices and guidance for Solution & Domain Architects.
• Lead AI technology radar to monitor emerging, maturing, and declining tech.
• Provide enablement and hands-on guidance to architecture teams.
• Collaborate with Security, Legal and Risk on guardrails for AI workloads.
• Serve as a standing member of architecture governance for AI strategy and investment decisions.
• Travel 10-15%.

Skills

AI architecture leadership

Enterprise standards setting

Solution & Domain architecture

Governance and guardrails

Model orchestration

Education

Bachelor's degree in Computer Science or Information Technology

18+ years technology delivery experience

Job description

The Principal Practice Architect, AI is the architecture decision maker for artificial intelligence across the enterprise. The role owns the standards, reference architecture, guardrails, technology selections and roadmap that every portfolio conforms to when it builds with AI. When this role sets the enterprise standard for an agent framework, a model gateway or an evaluation approach, that is the standard: Portfolio Architects design within it, Solution & Domain Architects conform to it and platform teams implement it. It works with senior IT, data, security and risk leaders as a peer.

• Define the enterprise agentic strategy and own the standards, patterns and reference architectures for AI: agentic patterns, model orchestration and gateways, retrieval and grounding, evaluation harnesses, and how AI workloads use the shared data and integration foundation
• Own AI technology and platform selection for the enterprise: model providers, agent frameworks, orchestration, evaluation and observability tooling, and AI code assist tooling, in consultation with the Data, Integration and Infrastructure Practice Architects
• Own Security and Responsible AI guardrails with Security, Legal and Risk: model risk classification, evaluation criteria, human-in-the-loop requirements, data protection for AI workloads and the conditions under which an AI capability may reach production
• Own the enterprise AI platform strategy and roadmap: build versus buy, platform consolidation, capability expansion, version currency and sunset planning, as architecture input to investment decisions
• Review solution designs that incorporate AI for conformance to the standard, and advise Portfolio Architects where their decisions intersect AI without overriding them
• Keep the AI Center of Excellence's platform delivery aligned to the enterprise standard, and provide architecture direction for the Enterprise AI Game Changers initiatives
• Define the AI governance and operating model: how AI use cases are proposed, approved, built and monitored, and how the AI Center of Excellence, portfolios and architecture divide the work
• Run the AI technology radar: assess what is emerging, maturing or declining, at a cadence that matches a market that resets every few quarters
• Document and publish AI best practices: tooling, design patterns and LLM guidance, and build AI architecture capability across Solution & Domain Architects through enablement and hands-on guidance
• Serve as a standing member of architecture governance, contributing AI intelligence to enterprise strategy and investment decisions

This is a remote position.

This position is not eligible for sponsorship for work authorization now or in the future, including conversion to H1-B visa.

Job duties include contact with other employees and access confidential and proprietary information and/or other items of value, and such access may be supervised or unsupervised. The Company therefore has determined that a review of criminal history is necessary to protect the business and its operations and reputation and is necessary to protect the safety of the Company's staff, employees, and business relationships.

Must be eighteen years or older

Minimum Qualifications:

• Must be at least 18 years of age
• Must be legally authorized to work in the country of employmentwithout now or in the future requiring sponsorship for employment visa status (e.g., OPT, CPT, H1B, EB-1, etc.)
• Bachelor's degree in Computer Science or Information Technology, or in lieu of a degree, at least 18 years of experience in technology delivery.
• 15+ years in technology, including 5+ years of hands-on software or machine learning engineering and 8+ years in architecture or deep technical leadership roles
• Production experience designing and governing AI systems at enterprise scale: LLM applications, agent frameworks, model orchestration, retrieval and grounding, evaluation harnesses and guardrails. Advisory-only AI experience does not qualify
• 3+ 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, enough to design AI workloads that reuse the existing foundation
• Current hands-on use of AI code assist and agentic development tooling; evaluates 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

Preferred Qualifications:

• TOGAF Level 2, CISSP, CEAP, CSA, CSEA

Travel:

10-15%