AI Governance Engineer – Standards & Controls

Glint Tech Solutions•Buffalo, NY

16h•Remote

About The Position

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, and producing documentation and automation that make governance operational.

Requirements

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

Nice To Haves

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)

Responsibilities

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