Enterprise Architect

Insight Global; Cincinnati (OH); On-site; USD 140,000 - 190,000; Full time

Job summary

Insight Global is seeking an Enterprise Architect who is deeply hands-on with AI-native technologies and agentic workflows to steer architectural decisions across the enterprise. The role encompasses build-versus-buy decisions, data and system integration, and a governance framework designed to prevent sprawl and fragmentation while enabling safe AI experimentation.

The successful candidate will lead enterprise patterns for AI-native operating models, knowledge management, and policy

Qualifications

• 8+ years in enterprise, platform, or application architecture roles with breadth across IT domains.
• Hands-on practice with AI-native models, agents, and decision-making workflows.
• Experience with API-first integration, data streaming, and canonical data models.
• Cloud architecture including AWS, landing zones, and serverless runtimes.
• Familiarity with TOGAF, C4, Zachman, FEAF as practical tools; TOGAF cert a plus.
• Knowledge management for AI world: docs, standards, institutional knowledge for machines.

Skills

AI-native architecture

Agent orchestration

Cloud architecture AWS

Data integration

API contracts

TOGAF & frameworks

Governance & security

Enterprise architecture leadership

Knowledge management

MLOps governance

Low-code governance

Job description

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 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.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [e-mail] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Skills and Requirements

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.