Vice President, AI Risk and Governance

LPL Financial•Charlotte, VA

1d•$137,093 - $228,454

About The Position

LPL Financial is seeking a Vice President to own the firm’s enterprise AI governance strategy and provide authoritative oversight and credible challenge across the AI lifecycle. The leader will establish the governance framework, risk standards, decision rights, and oversight expectations needed to support responsible AI adoption while protecting the firm, its clients, and its regulatory standing. This role requires understanding how AI solutions are designed, trained or configured, integrated, deployed, and monitored, and independently assessing risks and controls. The Vice President will challenge business and technical teams, drive decisions on approval, conditions, remediation, escalation, or risk acceptance, advise senior executives and governance committees, and represent the program in regulatory, audit, and other independent reviews. The position owns and evolves the enterprise AI Governance Framework and target operating model, translates regulatory developments and emerging risks into practical requirements, and reviews a broad range of AI and machine-learning use cases. The role evaluates architecture, models, data, integrations, security, third parties, controls, and operating processes; leads governance forums and risk-based decisions; defines accountability across relevant functions; and establishes risk indicators, executive reporting, and remediation oversight. It also builds and leads a high-performing AI governance team and promotes timely, practical governance that enables responsible innovation. LPL Financial is a wealth management firm supporting financial advisors and financial institutions. The company describes its purpose as helping turn life’s aspirations into financial realities.

Requirements

Bachelor’s degree in Computer Science, Data Science, Engineering, Information Systems, Risk Management, Finance, Law, Public Policy, or a related discipline.
10+ years of progressive experience in technology risk, model risk, artificial intelligence governance, cybersecurity, data risk, compliance, audit, or a related control discipline within financial services or another highly regulated industry.
5+ years of direct experience reviewing, governing, validating, auditing, implementing, or overseeing artificial intelligence, machine learning, advanced analytics, or model-based solutions.
Demonstrated experience challenging AI solutions at the architecture, model, data, and control levels, and explaining how solutions function, where risk originates, and whether proposed controls are sufficient.
Proven experience establishing or materially enhancing an enterprise governance, risk, or control framework, rather than solely advising on or documenting one.
Advanced working knowledge of AI and machine-learning concepts, including generative AI, large language models, retrieval-augmented generation, AI agents, training and inference, prompt-based systems, model evaluation, monitoring, and third-party AI services.
Deep understanding of AI risk domains, including model performance, explainability, bias and fairness, data quality and lineage, privacy, cybersecurity, intellectual property, recordkeeping, consumer protection, third-party dependency, resilience, and operational integration.
Ability to ask precise questions, test assumptions, identify missing information, and distinguish evidence from assertion.
Ability to convert complex and sometimes conflicting technical, legal, regulatory, and business considerations into clear recommendations and defensible decisions.
Executive communication skills, including explaining technical risk without jargon, framing tradeoffs, and producing concise materials that support action.
Leadership and influence skills to drive accountability across a matrixed organization, resolve ambiguity in roles and decision rights, and maintain independence while building productive partnerships.
Execution discipline to set standards, close gaps, escalate when necessary, and measure governance outcomes.
Demonstrated ability to make and defend difficult risk recommendations when facts are incomplete, stakeholders disagree, or business pressure is significant.
Experience advising executives and governance committees and representing a program in regulatory examinations, Internal Audit reviews, or comparable independent challenge.
Prior people leadership experience, including building capability, setting clear standards, and holding teams and stakeholders accountable for execution.

Nice To Haves

Advanced degree.
Experience leading AI governance, responsible AI, AI assurance, model risk, AI audit, or emerging technology risk within a broker-dealer, wealth management firm, bank, insurer, or other regulated financial institution.
Hands-on experience with AI development, model validation, technical architecture, technology risk assessment, control testing, or AI assurance.
Knowledge of the NIST AI Risk Management Framework, ISO/IEC 42001, ISO/IEC 23894, model risk management principles, and emerging U.S. and global AI regulatory expectations.
Relevant professional certification such as AIGP, CRISC, CISSP, CISM, CISA, FRM, or comparable credential.

Responsibilities

Own and evolve the enterprise AI Governance Framework, including policies, standards, risk taxonomy, risk-tiering methodology, approval authorities, decision rights, lifecycle requirements, monitoring expectations, and escalation protocols.
Set the strategic direction and target operating model for AI governance, translating regulatory developments and emerging AI risks into practical, examination-defensible requirements.
Provide independent review and credible challenge of AI and machine-learning use cases, including generative AI, large language models, AI agents, predictive models, embedded vendor capabilities, and other AI-enabled solutions.
Evaluate AI solutions at the architecture, model, data, integration, security, third-party, control, and operating-process levels to identify material risks, unsupported assumptions, control weaknesses, and residual exposure.
Challenge business and technical owners on use-case design, data suitability and lineage, model behavior, testing, validation, human oversight, guardrails, access, monitoring, incident response, change management, and decommissioning.
Chair or lead enterprise AI governance forums and drive risk-based decisions, documenting, assigning, monitoring, and escalating material conditions, exceptions, risk acceptances, and unresolved matters.
Serve as the senior AI governance advisor to executive leadership and provide concise recommendations on whether, and under what conditions, AI capabilities should proceed.
Establish clear accountability across Business, Technology, Information Security, Data Governance, Privacy, Legal, Compliance, Model Risk, Third-Party Risk, Enterprise Risk Management, and Internal Audit.
Define key risk indicators, performance measures, portfolio insights, and executive reporting on AI exposure, control effectiveness, exceptions, issues, emerging risks, and program maturity.
Oversee regulatory examinations, Internal Audit reviews, independent assessments, and other AI governance inquiries, and ensure identified gaps lead to sustainable remediation.
Build and lead a high-performing AI governance team with the judgment, technical fluency, and discipline needed for a credible second-line oversight program.
Promote responsible innovation through timely, practical governance that distinguishes manageable risk from unacceptable exposure.