AI Governance Specialist

Position: Responsible AI Governance Specialist
Responsible AI Principles Alignment

The Responsible AI Governance Specialist will help ensure Responsible AI principles are understood, internalized, and consistently applied across teams involved in AI design, development, deployment, monitoring, and governance. This includes supporting practical alignment to five core principles:

+ Evaluate the real-world impact of AI solutions on people.

+ Prevent the creation or reinforcement of unfair bias.

+ Support transparency and explainability in how AI solutions work.

+ Promote accountability through meaningful human oversight.

+ Respect privacy, protect intellectual property, and champion robust data governance.

Key Responsibilities

+ 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.

+ Translate technical AI and machine learning details into clear governance documentation for non-technical, compliance, legal, audit, and executive audiences.

Required Qualifications

+ 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.

+ Ability to translate policy, regulatory, and risk requirements into operational processes, documentation standards, controls, and review workflows.

+ Excellent written communication skills, with the ability to create clear, structured,…