Enterprise Architect

Enterprise Architect

Required

Skills & Experience:

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 Dev Ops grounding to be credible with engineers.

Nice to Have

Skills & Experience:

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.

Job Description:

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 y 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.