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Job ID R0120061 Date posted 10/02/2026
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
The Executive Director, Agentic Core IT Processes leads the transformation and modernization of USAA's core IT processes through the adoption of Agentic AI, intelligent automation, and modern software engineering practices. This leader is responsible for defining and executing the strategy, governance, and operating model for AI agents across the end-to-end Software Development Lifecycle (SDLC), including planning, development, testing, deployment, operations, and compliance. Partnering closely with Engineering, Infrastructure, Information Security, Architecture, Risk, and business leaders, the Director drives enterprise initiatives that improve delivery speed, quality, operational efficiency, and risk management while establishing the controls, metrics, and governance frameworks required to scale AI-enabled capabilities securely and compliantly across the organization.
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: San Antonio
What you'll do:
- Performs workload management and prioritization duties in support of operations for the functional area assigned.
- Applies subject-matter-expert functional knowledge to lead subordinate workers in producing work deliverables in support of departmental initiatives.
- Develops functional policies, procedures and guidelines.
- Identifies opportunities and facilitates major improvements to processes and systems.
- Ensures that the assigned application group develops processes and tools that contribute to the company business objectives.
- Ensures that all application processes developed internally or by external service providers are conducted in line with corporate social responsibility, environmental and technical policies and applicable standards and legislation.
- Oversees the assessment of the technical and business fitness of the application portfolio and their associated costs and risks In partnership with CTO, Infrastructure and IT Security, ensures that the organization's applications are effectively secured and that risks are mitigated and comply with legal and corporate privacy and confidentiality rules.
- Establishes management routines to ensure appropriate oversight of the organization's software development and maintenance activities.
- Serves as financial steward for the organization and manages relationships with major vendors and service providers to ensure they cost-effectively meet the needs of the organization.
- Evaluates and selects third party vendors to provide value added services. Facilitates the contracting process through the Third-Party Risk Management (TPRM) enterprise process.
- Responsible for contract company/service performance providing timely feedback to ensure the best value for USAA.
- Collaborates with CTO to ensure that solutions are consistent with technology standards.
- Works across IT and business partners to reduce technical debt.
- Collaborates with other leaders and/or stakeholders and develop an application development strategy that aligns with business strategy, adds value and is within budget constraints.
- Responsible for team's adherence to SDLC SDLC and applicable controls.
- Works to ensure teams have appropriate, development plans consistent with domain of expertise.
- Mentors across the technical community.
- Provides oversight to the engagement with the business DPO and ensures deliverables adhere to Agile practices/methodologies.
- Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.
What you have:
- Bachelor's degree in Computer Science, Information Systems, or related field; OR 4 years of relevant education and/or experience.
- 10 years of experience of general IT management competencies.
- 4 years of people leadership experience in building, managing and/or developing high-performing teams required.
- Strong technical background of web-based application architecture, system and database architecture, security application integration and object-oriented languages in a large enterprise environment.
- Extensive experience in all stages of the software development life cycle: requirements gathering, design, development, testing, and implementation.
- Demonstrated ability to communicate technical information to a non-technical audience.
- Experience collaborating with key resources and stakeholders, influencing decisions and managing work to achieve strategic goals required.
- Demonstrated experience in vendor contract management and management of distributed development teams and resources.
- Demonstrated financial acumen involving budgets, forecasting, and executing on the budgets for applicable technology support function.
- Understanding of relevant industry frameworks, i.e.COBIT, ITIL, SAFe, etc.
- Understanding of Legislative and Regulatory Compliance Requirements.
- Experience overseeing the technology life cycle from requirements analysis, feasibility estimates, design, code, documentation, testing, implementation, and support.
- Technical knowledge and understanding of technical domains that reside within Infrastructure Services, Security, Data or Application Development.
- Experience conducting cost benefit analyses and leveraging results to drive technology support solutions.
- Demonstrated thought leadership in embedding intuitive story telling of technology support functions including concise presentation of complex technical details.
- Experience and ability to drive a culture of quality and personal accountability through technology support teams.
What sets you apart:
- 10+ years of experience leading software engineering, software delivery, DevSecOps, platform engineering, or SDLC transformation functions.
- 5+ years of leadership experience managing technology organizations and enterprise-scale transformation initiatives.
- Deep end-to-end understanding of the software development lifecycle, including requirements management, architecture, design, development, testing, release management, deployment, operations, and continuous improvement.
- Demonstrated experience implementing modern software delivery practices including Agile, DevSecOps, CI/CD, platform engineering, software quality engineering, and engineering automation.
- Hands-on experience utilizing AI-assisted development capabilities, engineering copilots, autonomous workflows, agentic AI platforms, or intelligent automation solutions within the software development lifecycle.
- Experience defining, governing, and operationalizing AI Agents in enterprise environments, including agent lifecycle management, security controls, monitoring, auditability, and risk management.
- Experience developing governance frameworks and operating models for AI-enabled business or technology capabilities.
- Demonstrated ability to lead large-scale technology modernization initiatives involving organizational change, process redesign, and technology transformation.
- Experience integrating software delivery, developer productivity, security, testing, and operational tooling into end-to-end engineering platforms.
- Knowledge of AI risk management frameworks, Responsible AI principles, model governance, and applicable regulatory requirements.
- Experience establishing policy-as-code, automated controls, engineering guardrails, and compliance automation within software delivery pipelines.
- Demonstrated ability to communicate complex technical, operational, and AI-related concepts to senior executives, regulators, auditors, and technical teams.
- Experience managing vendor partnerships and evaluating emerging AI technologies and platforms.
- Working knowledge of industry frameworks including NIST AI RMF, NIST SSDF, CMMI, ITIL, COBIT, SAFe, and DevSecOps best practices.
- US military experience through military service or a military spouse/domestic partner.
Compensation range:
The salary range for this position is: $195,230 - $351,410.
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.
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.
Long Term Incentive Plan: Cash payment for Executive level roles only, representing a cash payment which is both time and performance based.
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 $195K-$351K 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 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 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($273K) sits 29% above the category median. Disclosed range: $195K to $351K.
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.
USAA AI Hiring
USAA has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span San Antonio, TX, US, Colorado Springs, CO. Compensation range: $351K - $351K.
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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