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Position Summary
The SVP, Technology Risk & AI Governance is the enterprise leader accountable for enabling the responsible, secure, and scalable use of artificial intelligence. This executive will set the vision, operating model, policies, guardrails, and oversight needed to accelerate AI adoption while managing model, data, privacy, cybersecurity, ethical, compliance, and third-party risks.
The role requires a proven technology risk executive. Deep experience leading enterprise technology, cybersecurity, data, controls, and regulatory risk programs is a foundational expectation and the platform from which this leader will build and mature AI governance. The primary mandate is to translate that risk leadership into practical AI governance that protects the enterprise without slowing responsible innovation.
The leader will serve as a trusted advisor and independent risk partner to senior executives, governance committees, and the Board, working across Technology, Information Security, Data, Legal, Compliance, Internal Audit, Model Risk, Procurement, and Business Operations.
Key Responsibilities
Enterprise AI Governance & Responsible AI Leadership
- Own and mature the enterprise AI governance strategy, framework, operating model, policies, standards, and decision rights.
- Establish practical guardrails and risk-based approval paths for AI development, acquisition, deployment, monitoring, material change, and retirement.
- Oversee AI risk assessments spanning model performance, data quality, privacy, cybersecurity, intellectual property, bias, fairness, transparency, explainability, human oversight, and regulatory compliance.
- Lead governance for generative AI, machine learning, large language models, agentic AI, and other emerging AI capabilities.
- Maintain comprehensive AI inventories, use-case classifications, lifecycle controls, exception processes, and evidence of accountable ownership.
- Define enterprise metrics, key risk indicators, dashboards, and escalation protocols that provide a clear view of AI risk exposure and control effectiveness.
- Monitor changes in AI regulation, supervisory guidance, industry standards, and market practices, and translate them into timely enterprise action.
- Advise business and technology leaders on how to innovate responsibly, balancing speed, client and employee value, and risk.
AI Risk, Controls & Monitoring
- Design and maintain an integrated AI risk and control framework aligned with enterprise risk management and existing technology, data, privacy, cybersecurity, compliance, and model risk programs.
- Establish testing, validation, ongoing monitoring, issue management, remediation, and continuous controls monitoring practices for AI solutions.
- Create clear accountability for AI outcomes across business owners, developers, vendors, control functions, and governance bodies.
- Oversee risk management for third-party AI providers, embedded AI capabilities, data sources, and foundation models, including due diligence and ongoing monitoring.
- Identify emerging AI risks early and drive coordinated mitigation across the enterprise.
Technology Risk Leadership
- Lead and continuously strengthen the enterprise Technology Risk Management framework, policies, standards, risk appetite, key risk indicators, reporting, and escalation processes.
- Provide independent oversight and effective challenge across technology strategy, cloud adoption, cybersecurity, data governance, digital transformation, third-party technology, and operational resilience.
- Drive integration of technology and AI risks into enterprise risk assessments, RCSA, compliance, audit, issue management, and control assurance programs.
- Present clear risk insights, trends, decisions, and recommendations to senior leadership, governance committees, regulators, auditors, and Board-level stakeholders.
Regulatory, Governance & Stakeholder Leadership
- Ensure AI and technology risk programs align with applicable regulatory expectations and recognized industry frameworks.
- Serve as the senior management liaison for examinations, audits, and regulatory reviews involving AI, technology, cybersecurity, data, and operational resilience.
- Partner with Legal, Compliance, Privacy, Information Security, Internal Audit, Model Risk, and Data Governance to build sustainable and coordinated oversight.
- Clarify decisions, ownership, and escalation across governance forums so that accountability is visible and action is timely.
Leadership & Capability Building
- Build and lead a high-performing team of AI governance, technology risk, controls, and emerging-risk professionals.
- Develop enterprise capability in responsible AI, emerging technology risk, advanced monitoring, and risk-informed decision making.
- Foster a culture of accountability, transparency, curiosity, constructive challenge, and responsible innovation.
- Influence senior stakeholders and drive adoption of consistent governance practices across business and technology teams.
Required Qualifications
- 10 or more years of progressive leadership experience across technology risk, cybersecurity risk, operational risk, model risk, internal audit, governance, risk and compliance, or related disciplines, including significant enterprise-level responsibility.
- Demonstrated experience designing, implementing, or materially advancing enterprise AI governance and responsible AI programs.
- Hands-on knowledge of AI lifecycle risks and controls, including generative AI, machine learning, large language models, data, privacy, cybersecurity, third-party risk, transparency, explainability, human oversight, and regulatory compliance.
- Deep expertise in technology risk management, control design and testing, cloud risk, cyber risk, data governance, third-party risk, operational resilience, issue management, and regulatory engagement.
- Proven ability to convert complex or evolving risk requirements into clear, practical guardrails that enable responsible innovation.
- Experience advising executive committees, governance forums, regulators, auditors, and Board-level stakeholders.
- Strong executive presence, judgment, influence, and communication skills, with the ability to create clarity across technical, risk, legal, and business audiences.
- Bachelor’s degree in information technology, computer science, cybersecurity, risk management, business, data science, or a related field.
Preferred Qualifications
- Financial services, wealth management, banking, insurance, or another highly regulated industry.
- Experience aligning AI governance to frameworks such as the NIST AI Risk Management Framework and integrating AI risk into enterprise risk management.
- Experience with AI assurance, model validation, automated controls monitoring, or governance technologies.
- Advanced degree or relevant professional certifications such as CRISC, CISA, CISSP, CGEIT, CISM, or equivalent.
The Leadership Profile We Are Seeking
An AI governance leader first, grounded in deep technology risk expertise. The successful candidate will be equally credible with technical teams, control functions, regulators, and executives; will bring strong judgment in an evolving environment; and will know when to set a firm boundary, when to ask a better question, and when to create a faster, safer path forward.
Pay Range:
$172,525.00 - $287,473.00 ###
Actual base salary varies based on factors, including but not limited to, relevant skill, prior experience, education, base salary of internal peers, demonstrated performance, and geographic location. Additionally, LPL Total Rewards package is highly competitive, designed to support your success at work, at home, and at play – such as 401K matching, health benefits, employee stock options, paid time off, volunteer time off, and more. Your recruiter will be happy to discuss all that LPL has to offer! Company Overview:
LPL Financial Holdings Inc. (Nasdaq: LPLA) is among the fastest growing wealth management firms in the U.S. As a leader in the financial advisor-mediated marketplace(6) , LPL supports over 32,000 financial advisors and the wealth management practices of approximately 1,100 financial institutions, servicing and custodying approximately $2.3 trillion in brokerage and advisory assets on behalf of approximately 8 million Americans. The firm provides a wide range of advisor affiliation models, investment solutions, fintech tools and practice management services, ensuring that advisors and institutions have the flexibility to choose the business model, services, and technology resources they need to run thriving businesses. For further information about LPL, please visit www.lpl.com.
At LPL, independence means that advisors and institution leaders have the freedom they deserve to choose the business model, services, and technology resources that allow them to run a thriving business. They have the flexibility to do business their way. And they have the freedom to manage their client relationships, because they know their clients best. Simply put, we take care of our advisors and institutions, so they can take care of their clients.
For further information about LPL, please visit www.lpl.com.
Join the LPL team and help us make a difference by turning life’s aspirations into financial realities. Please log in or create an account to apply to this position. Principals only. EOE.
Information on Interviews:
LPL will only communicate with a job applicant directly from an @lplfinancial.com email address and will never conduct an interview online or in a chatroom forum. During an interview, LPL will not request any form of payment from the applicant, or information regarding an applicant’s bank or credit card. Should you have any questions regarding the application process, please contact LPL’s Human Resources Solutions Center at (855) 575-6947.
EAC 5.19.26
Salary Context
This $172K-$287K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At LPL Financial, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. This role's midpoint ($229K) sits 8% above the category median. Disclosed range: $172K to $287K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
LPL Financial AI Hiring
LPL Financial has 9 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Positions span Fort Mill, SC, US, Fort Mill, SC, Austin, TX. Compensation range: $204K - $287K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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