Huron is redefining what a global consulting organization can be. Advancing new ideas every day to build even stronger clients, individuals and communities. We’re helping our clients find new ways to drive growth, enhance business performance and sustain leadership in the markets they serve. And, we’re developing strategies and implementing solutions that enable the transformative change they need to own their future.

As a member of the Huron corporate team, you’ll help to evolve our business model to stay ahead of market forces, industry trends and client needs. Our accounting, finance, human resources, IT, legal, marketing and facilities management professionals work collaboratively to support Huron’s collective strategies and enable real transformation to produce sustainable business results.

Join our team and create your future.

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The AI Infrastructure Engineer will build, integrate, and operate production platform capabilities that support Huron’s governed use of AI. Working in close partnership with the AI Infrastructure Architect across cloud infrastructure, model access, identity, observability, automation, agent execution, and enterprise service integration, this role will turn 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 building application-adjacent components such as mediation services, integration scaffolding, and reference implementations.

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Key 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 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, 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 the 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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 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 supp…