Principal Practice Enterprise Architect AI

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 Excellences 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 Companys 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.)
• Bachelors 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%

Required Experience:

Staff IC