AI Infrastructure Engineer (Senior Associate)

Huron Consulting Services•Chicago, IL

About The Position

The AI Infrastructure Engineer will build, integrate, and operate production platform capabilities that support Huron’s governed use of AI. Working closely with the AI Infrastructure Architect across cloud infrastructure, model access, identity, observability, automation, agent execution, and enterprise service integration, this role turns approved reference architectures and platform requirements into secure, reusable, and supportable solutions. The focus is infrastructure and platform engineering rather than application development, though the role includes application-adjacent components such as mediation services, integration scaffolding, and reference implementations. The role partners with the AI Capability Center under the Chief AI Officer to build and deliver infrastructure and platform components for Huron’s go-to-market teams. It also supports internal and client-facing teams with reusable onboarding templates and reference implementations, and helps establish operational practices, including runbooks, support models, incident paths, and handoffs. Flexible living locations are available across the United States, with travel as needed.

Requirements

At least 3 years of experience building and operating production cloud or platform infrastructure, including hands-on experience with AWS, infrastructure as code, automation, or production operations.
Solid infrastructure as code experience.
Experience with cloud networking, IAM, secrets management, logging, monitoring, deployment automation, and operational support.
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 take defined patterns and requirements, work through ambiguity with guidance from senior engineers and architects, and deliver working systems.
Ability to travel as needed.

Nice To Haves

Experience with Amazon Bedrock or other managed AI/model platforms.
Experience with token or cost telemetry, usage attribution, chargeback, showback, or FinOps data pipelines.
Experience with durable agentic AI orchestration platforms for 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.

Responsibilities

Work with the AI Infrastructure Architect to 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 from approved reference architectures.
Implement usage telemetry, token and model usage reporting, user and project attribution, quotas, and cost-management data pipelines.
Deploy and operate approved sandbox patterns for coding agents and tool-using agents.
Build governed tool and service mediation patterns for AI applications and agents, including MCP or equivalent approaches, following approved designs.
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.
Partner with the AI Capability Center under the Chief AI Officer to build and deliver infrastructure and platform components behind solutions for Huron’s go-to-market teams.
Use AI tools hands-on to accelerate infrastructure coding, automation, debugging, test creation, documentation, and runbook development.
Help define and maintain runbooks, support models, incident paths, and operational handoffs.