Principal AI Governance Architect

This is a fully remote position, open to applicants in United States.

📋 Description

• Convert security, privacy, compliance, and architectural requirements into actionable controls for AI workloads.

• Establish workload classification patterns along with necessary controls for each classification.

• Create patterns for prompt, response, embedding, retrieval, logging, retention, redaction, and client data segregation.

• Outline audit evidence patterns related to model access, data movement, retrieval, tool interactions, approvals, exceptions, and operational events.

• Develop identity, secrets, network, sandbox, logging, and approval-gate patterns tailored for AI applications and agents.

• Construct governed knowledge patterns that encompass authoritative sources, ingestion, indexing, metadata, access control, freshness, citation, and retrieval assessment.

• Choose the initial Huron Knowledge domain, source, or integration pattern for MVP validation.

• Define and execute metrics for retrieval quality, model evaluation patterns, regression checks, operational telemetry, dashboards, and quality reporting.

• Collaborate with infrastructure engineers to automate the implementation of controls, evidence, and reporting.

• Evaluate whether AI systems generate outputs that are useful, grounded, safe, auditable, and cost-effective.

• Leverage AI tools to expedite control design, policy mapping, knowledge analysis, evaluation design, dashboard creation, documentation, and evidence review.

⛳️ Requirements

• Over 8 years of experience in cloud security, platform security, governance engineering, security architecture, data engineering, observability, analytics engineering, ML evaluation, or monitoring AI applications.

• Comprehensive understanding of identity, network controls, secrets management, logging, audit trails, data classification, least-privilege design, and evidence capture.

• Knowledge of retrieval-augmented generation, embeddings, vector stores, metadata, indexing, citation, access control, and the quality of knowledge sources.

• Capacity to translate policy, risk, quality, and observability requirements into effective engineering controls and metrics.

• Proficient in software, data engineering, automation, or analytics engineering.

• Proven capability to utilize AI tools for governance engineering, analysis, dashboard creation, evaluations, documentation, or control reviews.

• Excellent documentation and communication skills for control standards, decision records, dashboards, exception patterns, and audit evidence.

• Preferred experience in AI governance, model risk management, LLM application security, agent security, or data protection for AI systems.

• Familiarity with Amazon Bedrock, AWS IAM, CloudTrail, CloudWatch, PrivateLink, KMS, VPC design, OpenSearch, vector databases, BI tools, or observability platforms is preferred.

• Prior experience in LLM evaluation, prompt evaluation, retrieval evaluation, golden datasets, regression testing, or AI quality frameworks is preferred.

• Experience with Temporal or similar workflow orchestration platforms is preferred.

• Background in enterprise knowledge systems, document repositories, metadata governance, search relevance, or permission-aware retrieval is preferred.

• Experience in environments dealing with PHI, PII, client-confidential, regulated, or sensitive data is preferred.

• Flexibility in living locations throughout the US.

• Willingness to travel as required.

🏝️ Benefits

• Annual incentive compensation program.

• Medical, dental, and vision coverage for employees and their dependents.

• Additional wellness programs.

• 401(k) plan featuring a generous employer match.

• Employee stock purchase plan.

• Comprehensive Paid Time Off policy.

• Paid parental leave.

• Adoption assistance.

• Complimentary annual health screenings and coaching.

• On-site banking services.

• Workshops held on-site.

• Continuous programs acknowledging significant events in employees’ lives.