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Principal AI Governance Architect

Huron Consulting · Chicago, IL · $190,000–$265,000 a year · Full time

Posted Aug 4, 2026 · We last checked this listing on Oct 6, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

Tell me about a time you turned a written policy from security or legal into a control that was enforced in code.

What they are checking: Whether they have actually built executable controls rather than writing more policy documents.

How do you work with privacy, compliance and security teams that each want different things?

What they are checking: Whether they can partner with control functions and find designs that satisfy all of them.

What would you do

A practice team wants to launch an AI agent next week that calls internal tools. Governance is not ready. What do you do?

What they are checking: Whether they find a safe path forward instead of either blocking everything or rubber-stamping risk.

You are building the first patterns that others will copy. How do you make sure they are easy to adopt?

What they are checking: Whether they design governance that teams will actually use, since hard patterns get bypassed.

About the work

How would you define workload classification for AI use at a consulting firm, and what controls would each class need?

What they are checking: Whether they can turn abstract risk into practical tiers that teams can actually follow.

Consultants want to use client data in a retrieval system. How would you design client data segregation?

What they are checking: Whether they understand that mixing client data in embeddings or retrieval is a serious confidentiality risk.

What audit evidence would you capture for model access, retrieval and tool calls, and how would you keep it from becoming a privacy problem itself?

What they are checking: Whether they balance logging for accountability with redaction and retention limits on sensitive data.

How would you design approval gates for AI agents that take actions, not just answer questions?

What they are checking: Whether they understand that tool-using agents need human checkpoints sized to the risk of the action.

How would you evaluate whether a knowledge assistant is giving accurate answers from Huron content?

What they are checking: Whether they can build evaluation and telemetry that catch quality problems before users lose trust.

What would you put on an AI operations dashboard for leadership?

What they are checking: Whether they know which signals show usage, risk and value rather than raw activity counts.

How would you handle prompt injection risk in a retrieval system that pulls from documents anyone can edit?

What they are checking: Whether they understand current AI attack patterns and design defenses rather than trusting the model.

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The full job description

As published by Huron Consulting.

Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation and navigate constant change. Through a combination of strategy, expertise and creativity, we help clients accelerate operational, digital and cultural transformation, enabling the change they need to own their future. Join our team as the expert you are now and create your future. The AI Security, Governance, Engineering and Observability role will translate security, privacy, compliance, architecture, and business requirements into executable platform controls while also establishing the first patterns for Huron Knowledge enablement, evaluation, telemetry, dashboards, audit evidence, and operational reporting. Key Responsibilities • Translate security, privacy, compliance, and architecture requirements into executable controls for AI workloads. • Define workload classification patterns and required controls for each class. • Establish prompt, response, embedding, retrieval, logging, retention, redaction, and client data segregation patterns in partnership with control functions. • Define audit evidence patterns for model access, data movement, retrieval, tool calls, approvals, exceptions, and operational events. • Design identity, secrets, network, sandbox, logging, and approval-gate patterns for AI applications and agents. • Build governed knowledge patterns for authoritative sources, ingestion, indexing, metadata, access control, freshness, citation, and retrieval evaluation. • Help select the first Huron Knowledge domain, source, or integration pattern for MVP validation. • Define and implement retrieval quality metrics, model evaluation patterns, regression checks, operational telemetry, dashboards, and quality reporting. • Partner with infrastructure engineers to implement controls, evidence, and reporting through automation rather than manual processes. • Help teams understand whether AI systems are producing useful, grounded, safe, auditable, and cost-effective outputs. • Use AI tools hands-on to accelerate control design, policy mapping, knowledge analysis, evaluation design, dashboard development, documentation, and evidence review. Required Qualifications • 8+ years of experience across cloud security, platform security, governance engineering, security architecture, data engineering, observability, analytics engineering, ML evaluation, or AI application monitoring. • Strong understanding of identity, network controls, secrets management, logging, audit trails, data classification, least-privilege design, and evidence capture. • Familiarity with retrieval-augmented generation, embeddings, vector stores, metadata, indexing, citation, access control, and knowledge-source quality. • Ability to translate policy, risk, quality, and observability requirements into practical engineering controls and metrics. • Strong software, data engineering, automation, or analytics engineering skills. • Demonstrated ability to use AI tools as a practical system-building accelerator for governance engineering, analysis, dashboard development, evaluation, documentation, or control review. • Strong documentation and communication skills for control standards, decision records, dashboards, exception patterns, and audit evidence. Preferred Qualifications • Experience with AI governance, model risk management, LLM application security, agent security, or data protection for AI systems. • Experience with Amazon Bedrock, AWS IAM, CloudTrail, CloudWatch, PrivateLink, KMS, VPC design, OpenSearch, vector databases, BI tools, or observability platforms. • Experience with LLM evaluation, prompt evaluation, retrieval evaluation, golden datasets, regression testing, or AI quality frameworks. • Experience with Temporal or comparable workflow orchestration platforms for approval flows, evidence capture, evaluation workflows, or operational reporting. • Experience with enterprise knowledge systems, document repositories, metadata governance, search relevance, or permission-aware retrieval. • Experience with PHI, PII, client-confidential, regulated, or sensitive-data environments. Flexible living locations across the US. Ability to travel as needed. The estimated base salary for this job is $190,000 - $265,000 USD. The range represents a good faith estimate of the range that Huron reasonably expects to pay for this job at the time of the job posting. The actual salary paid to an individual will vary based on multiple factors, including but not limited to specific skills or certifications, years of experience, market changes, and required travel. This job is also eligible to participate in Huron’s annual incentive compensation program, which reflects Huron’s pay for performance philosophy. Inclusive of annual incentive compensation opportunity, the total estimated compensation range for this job is $237,000 - $357,000 USD. The job is also eligible to participate in Huron’s benefit plans which include medical, dental and vision coverage and other wellness programs. The salary range information provided is in accordance with applicable state and local laws regarding salary transparency that are currently in effect and may be implemented in the future. Position Level Director Country United States of America

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