AI Infrastructure Architect - Senior Manager

Huron•Chicago, IL

4d•$210,000 - $294,000

About The Position

The AI Infrastructure Architect will serve as the technical architect and lead builder for the AI operations platform. This role defines and builds the platform patterns for governed model access, identity, networking, infrastructure as code, usage telemetry, project attribution, sandbox deployment, service mediation, and operational tooling. This is an expert builder/architect role who must be able to design the platform, implement critical components, guide technical decisions, provide day-to-day technical direction to platform engineers, and turn governance requirements into reusable infrastructure patterns. The emphasis is infrastructure and platform engineering, building the foundations that application and solution teams rely on, with hands-on contribution to application-adjacent components where needed. This role sits within AI Operations in Corporate IT, part of the CIO organization, and reports to the Director, AI Operations.

Requirements

8+ years of experience designing and building production cloud platforms, AI platforms, developer platforms, data platforms, or distributed systems, including 4+ years with AWS or equivalent cloud infrastructure.
Strong AWS architecture experience, including identity, networking, logging, observability, infrastructure as code, security boundaries, and production operations.
Strong hands-on engineering skills in at least one modern programming language and infrastructure automation framework.
Demonstrated ability to use AI tools as a practical system-building accelerator for platform engineering, automation, code generation, code review, testing, or documentation.
Experience with APIs, service integration, secure deployment patterns, event-driven systems, or platform automation.
Ability to design for security, auditability, cost visibility, operational reliability, and reuse.
Experience translating business, governance, and risk requirements into pragmatic engineering patterns.

Nice To Haves

Experience with Amazon Bedrock or equivalent enterprise model access platforms.
Experience with MCP or comparable governed tool/service mediation patterns.
Experience with durable agentic AI orchestration platforms for platform automation, agent workflows, or long-running AI operations.
Experience with sandboxed execution, coding agents, secure agent runtimes, or developer workspaces.
Experience with token/cost telemetry, usage attribution, chargeback, showback, or FinOps data pipelines.
Experience with regulated or client-confidential workloads.
Experience mentoring engineers or leading small technical workstreams.
Flexible living locations across the US. Ability to travel as needed.

Responsibilities

Serve as hands-on technical lead for AI reference architecture, workload patterns, control-plane boundaries, model access, agent execution, and integration decisions.
Define and build approved patterns for Amazon Bedrock, model access, networking, identity, logging, data flow, and production deployment.
Design core AI control-plane patterns for registration, policy enforcement, usage tracking, attribution, quotas, audit reporting, and exception handling.
Build reusable infrastructure as code, deployment templates, automation, sandbox patterns, MCP or equivalent service mediation, and operational tooling.
Lead implementation of usage telemetry, token reporting, user attribution, project attribution, and cost-management data pipelines.
Provide technical direction and mentorship to AI infrastructure engineers, including design and code review.
Partner with security, governance, knowledge, and observability functions to implement controls, knowledge access, evaluation, and audit evidence through automation.
Partner with the AI Capability Center under the Chief AI Officer to deliver the infrastructure and platform foundations, with targeted prototyping, behind solutions for Huron's go-to-market teams.
Use AI tools hands-on to accelerate architecture design, infrastructure coding, code review, testing, documentation, troubleshooting, and operational improvement.
Review and guide priority workload onboarding to ensure use cases follow approved architecture and operating patterns.
Document reference architectures, implementation patterns, runbooks, and engineering standards.