Enterprise AI Architect/Tech Enterprise Architect

Orangepeople•Plano, TX

About The Position

We are looking for an experienced Enterprise AI Architect / Technical Enterprise Architect to help define and advance enterprise-wide AI strategy, architecture, and technology direction. This is a senior architecture and technical leadership role requiring a strong Enterprise Architecture / Technical Architecture foundation combined with recent Enterprise AI and Generative AI experience. The successful candidate will work closely with the Enterprise Architecture team and cross-functional stakeholders to assess the current AI landscape, define target-state architecture and roadmaps, evaluate emerging AI technologies and vendors, and drive alignment around enterprise AI priorities. The ideal candidate will bring strong technical depth and the ability to challenge solution choices, evaluate architectural tradeoffs, and operate effectively across both technical strategy and stakeholder alignment.

Requirements

10+ years of experience in Enterprise Architecture, Technical Architecture, Solution Architecture, or related architecture leadership roles, with recent experience supporting Enterprise AI / Generative AI initiatives.
Strong Enterprise Architecture / Technical Architecture background, including current-state assessments, target-state architecture, capability mapping, technology strategy, and roadmap development.
Strong understanding of Generative AI, LLM architectures, Enterprise AI platforms, AI ecosystems, integration patterns, and emerging AI technologies.
Experience with Enterprise AI technologies and architectural patterns such as MCP, AI/LLM Gateways, agent platforms/registries, AI agents, skills/tool integration, or comparable technologies.
Demonstrated ability to assess and challenge architecture and technology decisions based on technical fit, scalability, security, cost, effort, complexity, and speed to implementation.
Experience with build-vs-buy analysis, product/technology evaluation, vendor assessment, and Proof of Concept initiatives.
Experience developing technology landscapes, capability maps, enterprise frameworks, architecture roadmaps, or similar strategic architecture artifacts.
Strong understanding of cloud architecture and enterprise cloud environments; AWS experience is highly valuable given the AWS-first environment, with Azure experience also beneficial where appropriate.
Understanding of security architecture, IAM, governance, privacy, compliance, and AI risk considerations within enterprise AI environments.
Strong ability to gather requirements across multiple teams, translate them into architecture requirements and measurable success criteria, and drive cross-functional stakeholder alignment and decision-making.
Excellent written and verbal communication skills, including the ability to communicate complex architecture concepts, recommendations, and tradeoffs to both technical and executive audiences.

Nice To Haves

AWS experience is highly valuable given the AWS-first environment
Azure experience also beneficial where appropriate

Responsibilities

Assess the current Enterprise AI landscape, including existing platforms, technologies, capabilities, stakeholder initiatives, architectural patterns, and gaps.
Help define the target-state Enterprise AI architecture, technology direction, roadmap, and prioritization approach.
Develop an Enterprise AI Taxonomy mapping AI capabilities and features to stakeholder initiatives across the enterprise AI landscape.
Establish and document a sustainable biannual Enterprise AI Landscape refresh process that captures evolving stakeholder requirements, emerging technologies, and industry developments.
Provide architecture leadership across Enterprise AI capabilities and technologies such as Generative AI, LLMs, Model Context Protocol (MCP), AI/LLM Gateways, agent platforms and registries, skills/tool integration, and related emerging AI technologies.
Evaluate existing and proposed AI technologies and architectures, challenging technical assumptions and technology choices while considering architectural fit, security, scalability, cost, implementation effort, and speed to value.
Conduct build-vs-buy analysis and technology/vendor evaluations, including defining evaluation criteria, comparing solutions, assessing architectural fit, and supporting Proof of Concept initiatives.
Define an MCP strategy in partnership with internal team members by gathering detailed stakeholder requirements, translating them into measurable success criteria, driving stakeholder review and sign-off, and supporting evaluation of the top two MCP vendors for Proof of Concept.
Partner with Enterprise Architecture, cloud, platform, security, application, data, AI, and other technology teams to align Enterprise AI initiatives with broader enterprise architecture standards and strategy.
Incorporate security, IAM, governance, privacy, compliance, and AI risk considerations into Enterprise AI architecture and technology recommendations.
Drive cross-functional stakeholder alignment and consensus, including Nemawashi-style collaboration, across teams with different requirements, priorities, and technology perspectives.
Recommend prioritization, ownership, sequencing, and implementation approaches for Enterprise AI capabilities and initiatives.
Communicate technical recommendations, architectural tradeoffs, risks, dependencies, and strategic decisions clearly to technical teams, stakeholders, and senior leadership.
Deliver an Enterprise AI Taxonomy mapped to capabilities and features each stakeholder is delivering in the AI landscape.
Develop a process for a bi-annual refresh of the Enterprise AI Landscape that allows continued sustainment of this landscape and capturing new requirements, as the industry evolves.
Define an MCP strategy with one of our Team members, documenting detailed stakeholder requirements, turning them into Success Criteria, reviewing with stakeholders for sign-off, and helping evaluate top 2 vendors for proof of concept.
Produce weekly status reports on the above work sent to TM
Participate in OP monthly team meetings and participate in team-building efforts.
Contribute to OP technical discussions, peer reviews, etc.
Contribute content and collaborate via the OP-Wiki/Knowledge Base.
Provide status reports to OP Account Management as requested.