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About This Role
Location Tampa, Florida; Charlotte, North Carolina
Job ID
R0118024
Date posted
07/22/2026
Why USAA?
At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the \#1 choice for the military community and their families.
Embrace a fulfilling career at USAA, where our core values – honesty, integrity, loyalty and service – define how we treat each other and our members. Be part of what truly makes us special and impactful.
We are proud to support active\-duty military spouses. USAA roles may offer remote or hybrid flexibility for active\-duty military spouses consistent with applicable policy and business needs.
The Opportunity
We are seeking a Lead Technology AI Risk Management Professional with extensive experience providing Second Line of Defense (SLOD) oversight for AI, Generative AI, data, and emerging technology initiatives within the financial services or insurance industry. The ideal candidate possesses deep expertise in technology risk, compliance, data governance, cybersecurity, and regulatory requirements, with a proven ability to assess complex AI use cases, challenge control effectiveness, and influence risk\-based business decisions. This individual will have strong knowledge of leading AI and GenAI platforms, including large language models (LLMs), open\-source technologies, Microsoft Copilot, OpenAI, Anthropic, and Amazon Q, as well as data protection, privacy, encryption, access management, and cloud\-based technologies. Working across business, technology, legal, compliance, and audit functions, the successful candidate will drive AI governance, provide credible challenge to senior stakeholders, identify emerging risks, and help ensure the responsible, secure, and compliant adoption of AI capabilities across the enterprise.
We offer a flexible work environment that requires an individual to be in the office 4 days per week. This position can be based in one of the following locations: Charlotte, NC, or Tampa, FL.
Relocation assistance is not available for this position.
What you'll do:
- Routinely communicates results of risk assessments to governance committees, business process owners and various levels of leadership and influences decision making.
- Develops processes and procedures for successful implementation of new risk policies, practices, appetites, and solutions to ensure holistic understanding and management of risks according to industry best practice.
- Identifies and seeks key stakeholders across the enterprise to support the identification, assessment, aggregation and the overall management of risks and controls.
- Crafts key communications related to risk and compliance insights to be delivered to executives and board members.
- Executes compliance risk management activities in accordance with enterprise compliance standards.
- Maintains and expands expert knowledge of the competitive/regulatory landscape and the company's key challenges. Keeps abreast of the competitive/regulatory landscape and shares expert knowledge w/team members.
- Coordinates and responds to regulatory requirements and requests and ensures the execution of examinations.
- Applies expert knowledge to utilize or produce analytical material for discussions with cross functional teams to understand business objectives and influence solution strategies.
- Leads, assembles, and facilitates cross\-functional teams to identify, assess, aggregate, and mitigate current and emerging risk events.
- Serves as the point of contact for senior risk leadership on projects and special management requests that often impact the enterprise or core operating area.
- Formulates and reviews stress test plans for a line of business or the enterprise.
What you have:
- Bachelor's degree; OR 4 years of relevant education and/or experience.
Experiences that will support your success:
- 8\+ years of progressive experience in risk management, compliance, technology risk, information security, audit, legal, data governance, or regulatory oversight within financial services, insurance, or other highly regulated industries, including experience supporting emerging technologies, AI, and Generative AI initiatives.
- Extensive experience leading risk and compliance programs within large, complex, and highly matrixed organizations, with a demonstrated ability to navigate competing priorities, influence stakeholders, and drive effective risk management outcomes across multiple business and technology functions.
- Expert knowledge of applicable laws, regulations, supervisory guidance, and industry frameworks, including those related to technology, data management, cybersecurity, model risk management, privacy, and the evolving regulatory landscape governing AI and Generative AI.
- Demonstrated ability to serve as a trusted advisor by translating regulatory requirements, emerging risks, and industry best practices into actionable guidance, governance requirements, and business\-focused risk solutions for executive leadership and key stakeholders.
- Proven experience providing credible challenge and independent oversight, including the ability to effectively influence decisions, resolve conflicts, negotiate risk treatment strategies, and drive accountability across all levels of management, including senior executives.
- Strong analytical, critical thinking, and problem\-solving capabilities, including experience leveraging data analysis techniques, risk metrics, key risk indicators (KRIs), trend analysis, and reporting to support fact\-based decision\-making and executive\-level risk insights.
- Preferred experience overseeing AI and Generative AI governance programs, including AI risk assessments, model governance, responsible AI controls, third\-party AI evaluations, AI regulatory compliance, and implementation of frameworks such as NIST AI RMF, ISO 42001, and enterprise AI governance standards.
- Advanced proficiency with Microsoft 365 and productivity technologies, including Word, Excel, PowerPoint, Teams, and Copilot, with experience developing executive presentations, risk dashboards, governance reporting, and data\-driven insights for senior leadership.
- Demonstrated leadership in balancing innovation with risk management, enabling responsible adoption of AI technologies while maintaining strong governance, regulatory compliance, customer trust, and organizational resilience.
What sets you apart:
- Demonstrated experience developing, implementing, or overseeing enterprise AI governance, responsible AI, and AI ethics frameworks, including policies, standards, risk appetite statements, control requirements, and governance processes aligned with regulatory and industry expectations.
- Proven ability to advise senior leaders and business stakeholders on AI\-related risks, including data privacy, explainability, fairness, transparency, bias mitigation, human oversight, and responsible use of AI and Generative AI technologies.
- Deep understanding of Second Line of Defense (SLOD) risk management, compliance, and governance practices supporting AI and GenAI initiatives within highly regulated financial services and insurance environments.
- Strong knowledge of AI/GenAI technologies and ecosystems, including large language models (LLMs), foundation models, AI agents, open\-source frameworks, and leading platforms such as Microsoft Copilot, OpenAI, Anthropic, Amazon Q, Azure OpenAI, and Google Gemini.
- Experience assessing and challenging AI solution designs, identifying technology and model risks, evaluating control effectiveness, and defining risk mitigation requirements that enable responsible innovation while protecting the enterprise.
- Familiarity with cloud\-native AI architectures and MLOps practices within AWS, Microsoft Azure, and Google Cloud Platform environments, including model deployment, monitoring, lifecycle management, performance validation, and operational risk considerations.
- Strong understanding of data governance, data risk management, and information protection principles, including data classification, encryption, access management, PII handling, data lineage, retention requirements, and compliance with privacy regulations and industry standards.
- Proven success leading complex projects, programs, or portfolios involving cross\-functional stakeholders across technology, risk, compliance, legal, audit, and business organizations.
- Expertise in evaluating and governing third\-party and cloud\-based AI/SaaS solutions, including security controls, vendor risk assessments, data protection requirements, resiliency considerations, and regulatory compliance obligations.
- Strong background in cybersecurity, information security, and technology risk management, including cloud security, secure development practices, identity and access management, cyber resilience, and emerging threats associated with AI and GenAI technologies.
- Experience applying risk and control frameworks such as NIST AI RMF, NIST CSF, ISO 42001, ISO 27001, FFIEC guidance, OCC expectations, NYDFS requirements, and emerging AI regulatory and supervisory standards.
- Professional certifications such as CRISC, CISM, CRCM, CIPP, FAIR, CISSP, or ABA Risk Management Certification are strongly preferred.
- AI and technology certifications such as ISACA Certified in Emerging Technology (CET), Microsoft Azure AI Fundamentals (AI\-900\), Azure AI Engineer Associate, IBM AI Engineering Professional Certificate, or comparable credentials are highly valued.
- Exceptional communication and executive influence skills, with a demonstrated ability to translate complex AI, cybersecurity, and technology risks into clear, actionable insights for senior leadership, risk committees, regulators, and auditors.
- Recognized as a trusted advisor capable of balancing innovation and risk management while shaping enterprise AI governance strategies, risk frameworks, and oversight programs that support safe and responsible AI adoption at scale.
Compensation range: The salary range for this position is: $143,320 \- $273,930.
USAA does not provide visa sponsorship for this role. Please do not apply for this role if at any time (now or in the future) you will need immigration support (i.e., H\-1B, TN, STEM OPT Training Plans, etc.).
Compensation: USAA has an effective process for assessing market data and establishing ranges to ensure we remain competitive. You are paid within the salary range based on your experience and market data of the position. The actual salary for this role may vary by location.
Employees may be eligible for pay incentives based on overall corporate and individual performance and at the discretion of the USAA Board of Directors.
The above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job.
Benefits: At USAA our employees enjoy best\-in\-class benefits to support their physical, financial, and emotional wellness. These benefits include comprehensive medical, dental and vision plans, 401(k), pension, life insurance, parental benefits, adoption assistance, paid time off program with paid holidays plus 16 paid volunteer hours, and various wellness programs. Additionally, our career path planning and continuing education assists employees with their professional goals.
For more details on our outstanding benefits, visit our benefits page on USAAjobs.com.
*Applications for this position are accepted on an ongoing basis, this posting will remain open until the position is filled. Thus, interested candidates are encouraged to apply the same day they view this posting.*
*USAA is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.*
Salary Context
This $143K-$273K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At USAA, 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 Required
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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($208K) sits 5% below the category median. Disclosed range: $143K to $273K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
USAA AI Hiring
USAA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tampa, FL, US. Compensation range: $273K - $273K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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