Senior AI Infrastructure Architect

Huron•Chicago, IL

1d•$201,000 - $306,000•Remote

About The Position

The Sr AI Infrastructure Architect will design, build, integrate, and operate production platform capabilities that support Huron's governed use of AI. Working across cloud infrastructure, model access, identity, observability, automation, agent execution, and enterprise service integration, this role will turn evolving platform requirements into secure, reusable, and supportable solutions.

Requirements

6+ years of experience building and operating production cloud or platform infrastructure, including hands-on experience with AWS, infrastructure as code, automation, or production operations.
Strong infrastructure as code experience.
Experience with cloud networking, IAM, secrets management, logging, monitoring, deployment automation, and operational support.
Strong software engineering or scripting skills for automation and platform tooling.
Demonstrated ability to use AI tools as a practical system-building accelerator for infrastructure automation, code generation, debugging, testing, or documentation.
Familiarity with APIs, event-driven systems, service integration, and secure deployment patterns.
Ability to work in ambiguous environments and convert platform requirements into working systems.

Nice To Haves

Experience with Amazon Bedrock or other managed AI/model platforms.
Experience with token/cost telemetry, usage attribution, chargeback, showback, or FinOps data pipelines.
Experience with Temporal or comparable workflow orchestration platforms for durable infrastructure automation, agent workflows, or operational processes.
Experience with containerized or sandboxed execution environments.
Experience with agent tools, MCP, model gateways, API gateways, or secure service broker patterns.
Experience supporting regulated, client-confidential, PHI, PII, or sensitive workloads.
Flexible living locations across the US.
Ability to travel as needed.

Responsibilities

Build and operate approved model-access patterns for governed AI usage, including Amazon Bedrock where applicable.
Build infrastructure as code, deployment automation, CI/CD templates, and reusable platform components.
Implement usage telemetry, token reporting, model usage reporting, user attribution, project attribution, quotas, and cost-management data pipelines.
Deploy and operate approved sandbox patterns for coding agents and tool-using agents.
Build governed tool/service mediation patterns for AI applications and agents, including MCP or equivalent approaches where applicable.
Implement logging, monitoring, alerting, audit evidence capture, and operational reporting for onboarded workloads.
Build onboarding templates and reference implementations that Client-facing AI Delivery, Enterprise IT, Global Products, analytics, consulting, engineering, and internal operations teams can reuse.
Use AI tools hands-on to accelerate infrastructure coding, automation, debugging, test creation, documentation, and runbook development.
Help define runbooks, support models, incident paths, and operational handoffs.