[Hiring] AI Governance Engineer – Standards & Controls @Glint Tech Solutions LLC

Role Description

The AI Governance Engineer serves as the technical production engine behind our client's AI standards and controls build-out, working under the direction of the AI Governance & Controls Lead. This is a technical governance role focused on:

• Drafting standards
• Engineering controls as implementable/testable requirements
• Building control-to-evidence mappings
• Producing documentation and automation that make governance operational

Key Responsibilities include:

• Draft and iterate the AI standards library: model onboarding/approval, oversight tiers, logging/retention, agentic guardrails, data classification, acceptable use
• Engineer each control as a testable requirement (definition, enforcement point, owner, evidence artifact, validation method)
• Build and maintain obligation-to-control-to-evidence mappings against regulatory guidance and internal policy
• Contribute to policy-as-code and controls-automation approaches with the platform team
• Produce evidence artifacts, control attestations, and assessment documentation for deviations/exceptions
• Validate platform-generated evidence (audit logs, entitlement records, quota enforcement) against control requirements
• Support deviation/exception lifecycle management: drafting, compensating controls, tracking, closure evidence
• Assemble components of examiner-readiness and audit-response packages
• Support AI Control Group, working group, and committee operations
• Maintain AI use case/agent registry data quality
• Document governance processes, runbooks, and templates for FTE handover
• Transfer working knowledge to bank FTEs throughout the engagement

Qualifications

• Minimum 5 years' experience in technology governance, IT risk/controls, security governance engineering, or compliance engineering
• Demonstrated experience writing technical standards or control frameworks mapped to regulatory/policy obligations
• Working technical knowledge of cloud platforms and modern application patterns (APIs/gateways, identity/RBAC, logging/monitoring, environment separation)
• Familiarity with AI/GenAI systems sufficient to write enforceable requirements for model access, oversight, and logging
• Strong technical writing and documentation discipline
• Experience with GRC processes: exceptions, findings, evidence collection, remediation tracking

Requirements

• Financial services or other highly regulated industry experience; familiarity with model risk management
• Experience with policy-as-code, compliance automation, or controls testing automation
• Familiarity with NIST AI RMF, SR 11-7/SR 26-2 lineage, or comparable AI governance frameworks
• Azure experience (APIM, Entra ID, Azure Monitor, Key Vault)