AI Governance Specialist

Partner with team leads to document AI systems, use cases, risks, controls, ownership, approvals, and governance processes. Maintain the AI use case inventory and related lifecycle documentation, ensuring records are complete, current, traceable, verifiable, and accessible. Compile and maintain model cards or technical documentation packages using existing design documents, architecture records, evaluation reports, release documentation, testing evidence, and approval records. Coordinate AI risk intake and tiering processes, ensuring required documentation, evidence, approvals, and escalation paths are captured. Produce governance reports with clear lineage from AI use cases, system documentation, risk assessments, control evidence, owner approvals, release decisions, and audit responses. Maintain evidence packages for AI labeling and user disclosure, including screenshots, user interface examples, and documentation showing where AI-generated content is disclosed to users. Document human intervention and feedback mechanisms, including user feedback loops, revision workflows, and how feedback is used to improve model or product quality. Document explainability and transparency practices, including Agentic AI and RAG architecture, Agentic RAG workflows, source citations, Shepard’s® validation, reasoning workflows, and grounding in trusted legal content. Track governance, testing, and quality assurance evidence, including offline evaluations, human evaluations, DDE quality ratings, regression testing results, release gates, production monitoring, and operational dashboard evidence. Support quarterly reviews and audits of AI systems and models to identify documentation gaps, control gaps, emerging risks, and required remediation actions. Drive follow-up across distributed teams to ensure governance records, control evidence, and remediation items remain complete, accurate, and current. Coordinate responses to AI governance, transparency, audit, legal, compliance, and risk management requests. Support the development, implementation, and continuous improvement of responsible AI and model risk policies, standards, procedures, and operating practices. Translate policy, regulatory, and governance requirements into practical operating processes that can be adopted by technical and business teams. Identify documentation gaps, ownership gaps, process risks, model risk concerns, and control weaknesses related to AI governance. Evaluate and apply tools that improve AI inventory management, governance documentation, model/system traceability, control evidence collection, risk tracking, and regulatory reporting. Stay current with emerging AI technologies, industry trends, responsible AI practices, global regulatory changes, model risk management expectations, and industry standards. Partner with Legal, Compliance, and Risk teams to translate applicable requirements into practical governance processes, documentation expectations, and evidence standards. Requirements 2+ years of hands-on experience building, evaluating, deploying, governing, or supporting large-scale AI, machine learning, or data science systems. Applied experience with AI/ML concepts, data science workflows, software delivery processes, and governance controls. Strong understanding of AI governance concepts and risk domains, including bias, fairness, explainability, privacy, security, transparency, and accountability. Familiarity with AI risk and governance frameworks, such as the NIST AI Risk Management Framework, responsible AI principles, model risk management practices, or similar frameworks. Knowledge of data privacy and regulatory requirements, including CCPA, GDPR, emerging AI regulations, and related compliance expectations. Ability to produce traceable and verifiable governance reports supported by clear evidence, ownership, approvals, and documentation. Excellent written communication skills, with the ability to create clear, structured, and audit-ready documentation. Strong analytical and problem-solving skills, with the ability to assess risks, identify gaps, and recommend practical improvements. Strong stakeholder management skills and the ability to drive cross-functional collaboration across technical and non-technical teams. Ability to use and stay current with the latest AI technologies, governance tools, regulatory developments, and industry practices. Core Competencies Demonstrates expertise in AI governance, risk management, and documentation practices, with a strong focus on compliance with regulatory requirements and the ability to produce clear, structured governance reports. Proficient in stakeholder management and cross-functional collaboration to ensure effective AI system oversight. Highest-signal resume keywords AI Governance Concepts Risk Management Frameworks Data Privacy Regulations Governance Documentation Stakeholder Management ATS Optimization Keywords Hard Skills AI System Evaluation Machine Learning Support Data Science Workflows Governance Controls Risk Assessment Model Risk Management Documentation Practices Evidence Collection Regulatory Reporting Audit-Ready Documentation Soft Skills Analytical Skills Problem-Solving Skills Written Communication Cross-Functional Collaboration Stakeholder Management Industry Keywords NIST AI Risk Management Framework Responsible AI Principles CCPA GDPR AI Transparency AI Explainability Bias and Fairness Privacy and Security Accountability Emerging AI Regulations Tools & Technologies Governance Tools AI Inventory Management Tools Regulatory Compliance Tools Model Traceability Tools Risk Tracking Tools #J-18808-Ljbffr