Principal AI Governance Architect

Huron•Chicago, IL

9d•$237,000 - $357,000•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

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.