VP, AI Governance & Responsible AI

LPL Financial•Charlotte, NC

15d•Hybrid

About The Position

We are seeking an experienced Vice President of AI Governance & Responsible AI to lead the development and implementation of comprehensive AI governance frameworks within our company. Reporting to the Senior Vice President of Data Governance & Responsible AI, this strategic leadership role requires 10+ years of expertise in AI governance, ethics, and risk management, with a deep understanding of the financial services’ regulatory requirements. The ideal candidate will establish responsible AI principles, drive ethical AI adoption, enable technical innovation and ensure compliance with evolving regulatory standards while enabling business value from AI technologies. This role will work at the intersection of technology, risk management, regulatory compliance, and business strategy, partnering with executive leadership, data science teams, legal, compliance, and business units to embed responsible AI practices throughout the organization.

Requirements

10+ years of progressive experience in AI governance, Responsible AI, AI Ethics, or related fields, with at least 5 years in Financial Services
Deep understanding of AI/ML technologies, including supervised and unsupervised learning, deep learning, natural language processing, and generative AI
Demonstrated expertise in developing and implementing AI governance frameworks in regulated financial services environments
Comprehensive knowledge of financial services regulations affecting AI systems, including model risk management, fair lending, consumer protection, and data privacy laws
Proven experience with AI risk management, including bias detection and mitigation, model validation, and adversarial robustness testing
Experience managing regulatory examinations, audits, or third-party assessments related to AI/ML systems
Bachelor’s degree in computer science, Data Science, Engineering, or related technical field required
AI Ethics & Responsible AI: Deep understanding of ethical frameworks for AI development and deployment, including fairness, accountability, transparency, and explainability principles
Governance & Risk Management: Expertise in designing and implementing governance structures, control frameworks, and risk management processes for AI systems
Technical Proficiency: Strong foundation in machine learning algorithms, model development lifecycle, MLOps practices, and AI infrastructure
Familiarity with emerging AI technologies including large language models, generative AI, and foundation models, and their governance implications
Regulatory Acumen: In-depth knowledge of financial services regulatory environment and ability to anticipate and respond to regulatory changes
Strategic Thinking: Ability to balance innovation with risk management, enabling responsible AI adoption while protecting the organization
Leadership & Influence: Track record of driving organizational change, building consensus among diverse stakeholders, and leading high-performing teams
Business Acumen: Understanding of financial services business models, products, and customer lifecycle to contextualize AI governance decisions
Analytical & Problem-Solving Skills: Ability to analyze complex technical and organizational challenges and develop pragmatic solutions
Strong technical background with ability to evaluate AI architectures, algorithms, and implementation approaches
Excellent communication skills with ability to translate complex technical concepts for diverse audiences
Experience leading cross-functional initiatives and influencing stakeholders at all organizational levels, including executive leadership and board members

Nice To Haves

Advanced degree (Master's or PhD) in Computer Science, Data Science, Statistics, Engineering, Ethics, Law, or related field
Professional certifications such as Certified Information Systems Security Professional (CISSP), Certified Information Privacy Professional (CIPP), or Certified AI Practitioner
Experience working with AI governance frameworks such as NIST AI Risk Management Framework, EU AI Act requirements, or ISO/IEC standards
Hands-on experience with AI/ML development tools and platforms (e.g., Python, TensorFlow, PyTorch, cloud ML services)
Experience implementing AI governance technology solutions such as model risk management platforms, bias detection tools, or ML observability systems
Background in multiple domains such as credit risk, fraud detection, algorithmic trading, or customer analytics within financial services
Experience with large-scale organizational transformation initiatives related to AI, data, or technology governance

Responsibilities

Design, implement, and continuously improve enterprise-wide AI governance frameworks that align with regulatory requirements, industry best practices, and organizational values
Establish and chair AI governance committees, including AI Ethics Boards and Model Risk Management forums, to oversee AI development, deployment, and monitoring
Develop responsible AI principles, policies, and standards addressing fairness, transparency, explainability, accountability, privacy, and safety
Create and maintain AI governance documentation including charters, playbooks, standard operating procedures, and decision frameworks
Ensure AI systems comply with financial services regulations including FCRA, ECOA, fair lending laws, model risk management guidance (SR 11-7), and emerging AI-specific regulations
Develop and implement AI risk assessment frameworks to identify, measure, monitor, and mitigate risks including algorithmic bias, model drift, data quality issues, and adversarial threats
Collaborate with Legal, Compliance, and Risk Management teams to interpret regulatory guidance and translate requirements into actionable technical controls
Lead regulatory examinations and audits related to AI systems, preparing documentation and responding to examiner inquiries
Establish model validation standards and processes for AI/ML models across the organization, including testing for bias, fairness, and robustness
Implement AI model inventory and lifecycle management systems to track models from development through deployment and retirement
Define and implement explainability and interpretability requirements for AI systems, ensuring stakeholders can understand model decisions
Partner with technology teams to embed governance controls in AI development platforms, MLOps pipelines, and production environments
Build partnerships across business units, technology, risk management, compliance, legal, and human resources to embed responsible AI practices
Develop and deliver training programs on responsible AI, AI ethics, and governance requirements for technical and non-technical audiences
Establish metrics and KPIs to measure the effectiveness of AI governance programs and responsible AI practices
Develop ongoing monitoring capabilities for deployed AI systems to detect performance degradation, bias drift, and compliance issues
Stay current with emerging AI technologies, governance frameworks, regulatory developments, and industry best practices, adapting organizational approaches accordingly