Enterprise Architect

A client is looking for an Enterprise Architect who is deeply hands-on with AI and agentic technologies, not someone who only understands them conceptually.

This person will have final authority over build-versus-buy decisions, determining what applications should be purchased, custom-built with AI agents, integrated into existing platforms, or not pursued at all.

They need someone who can prevent technology sprawl and data fragmentation by creating a cohesive enterprise architecture, defining system ownership, and enforcing standards across applications, data, integrations, and cloud environments.

A major focus is establishing a safe, governed framework for business users to experiment with AI, while ensuring only vetted ideas move into production through a structured approval process.

The ideal candidate must be able to design AI-native operating models, knowledge management strategies, agent guardrails, and enterprise-wide patterns that both humans and AI systems can consume and follow.

Ultimately, they want a strategic leader with strong executive presence who can balance enterprise architecture, AI innovation, governance, security, and business alignment while helping First Student transition into an AI-native organization.

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8+ years in enterprise, platform, or application architecture roles, with demonstrated depth and

breadth across multiple IT domains and production systems at enterprise scale.

• Demonstrable, current, hands-on practice working AI-native with frontier models and agents -

authoring skills, rules, harnesses, and agentic workflows, and using them as your actual method of

designing and deciding. This is measured by what you are doing now, not by years.

• Working understanding of AI architecture: agent orchestration and runtimes, control planes,

inference gateways and model routing, knowledge bases and context (RAG, MCP), evals, guardrails,

and AI/MLOps.

• Proven portfolio-level judgment: build-versus-buy decisions you owned, technical trade-off

analysis, and the ability to make a decision stick with a line of business that wanted the other

answer.

• Deep integration and data architecture experience - API-first contracts, event and data streaming,

canonical models, system-of-record ownership, and integration between enterprise systems such

as ERP, payroll and HR, and proprietary platforms.

• Experience architecting and integrating large-scale custom software products, internal and

customer-facing, and making a portfolio of distinct products operate as a coherent whole.

• Solid cloud architecture experience, ideally AWS - reference architectures, landing zones, cloud

shared services, well-architected design, containers and serverless runtimes, and automated or

dynamic provisioning.

• Design and guidance experience in knowledge management for an AI world: how documentation,

standards, and institutional knowledge are structured so machines can use them.

• Hands-on background across a full agile SDLC - discovery, scoping (PRDs and ADRs), delivery,

testing, deployment, and support - with enough full-stack and DevOps grounding to be credible

with engineers. Familiarity with architecture frameworks (TOGAF, C4, Zachman, FEAF) as practical tools rather than

as the job itself. TOGAF certification a plus.

• AWS certification, for example Solutions Architect - Professional.
• Experience governing citizen development or low-code platforms at enterprise scale, including

intake, promotion, and sunset.

• Experience consolidating or retiring redundant systems, and living with the organizational friction

that comes with it.

• Experience with docs-as-code, developer portals, knowledge graphs, or MCP servers as machine

facing knowledge surfaces.

• Experience with policy-as-code (for example OPA) and compliance-by-construction in regulated

environments.

• Platform-first architecture depth across a base platform and its child applications, and the shared

services they consume at both the cloud infrastructure and application tooling levels: auth and

accounts, messaging and notifications, search, files and media, and localization.

• Experience with EV technology, IoT, telematics, and on-bus or on-vehicle technology solutions.
• Experience with transportation, logistics, routing, geospatial, or operations-focused software.
• Experience applying AI in governed enterprise environments, including FERPA-relevant or similarly

regulated data.