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Job Summary
The Responsible AI Lead will establish and operationalize the enterprise’s approach to Responsible AI and AI governance and deployment across internally built and third\-party AI solutions. This role will help ensure AI is deployed safely, ethically, and effectively enterprise\-wide by creating the frameworks, processes, and organizational alignment needed to support enterprise adoption. This role helps establish a framework to turn “Responsible AI” into a business accelerator.
This is an exempt level role and reports to the VP Data \& AI.
About UFCU
Our Credit Union was founded in 1936 and has grown to serve members throughout Texas and beyond. At UFCU, we are more than just a financial institution, and our people are more than just employees. We are dedicated to our purpose of empowering our Members to achieve financial success and build brighter futures.
In pursuit of our aspiration that UFCU is loved by millions of Members and built to thrive for generations, we are guided by our values:
- Purposefully Member\-Obsessed: We are driven by a profound sense of empathy to deeply understand our Members’ needs and preferences, what brighter futures means to them, and the obstacles in their way. We act in our Members’ best interests, forever seeking to empower their financial success.
- Possibilities Reimagined: We are inspired to courageously experiment, learn, and iterate in pursuit of positive impact for our Members, UFCU, and coworkers. We challenge assumptions, embrace diverse perspectives, and make use of data and insights.
- Performance Excellence Rooted in Unwavering Integrity: We do the right thing, always. We champion teamwork, accountability, continuous improvement, and celebrate successful outcomes of others, fostering an inclusive environment of excellence and collaboration.
Essential Functions
- Own and operationalize the enterprise AI Risk Management and Responsible AI framework across the full AI lifecycle (intake, design, build/buy, testing/validation, deployment, monitoring, change management, and retirement) for both internally developed and third‑party AI solutions
- Maintain an “always current” governance posture by monitoring evolving laws, regulations, supervisory expectations, and standards (e.g., EU AI Act and other emerging guidance), translating updates into internal policy, controls, procedures, and playbooks
- Define and enforce enterprise decision rights for AI adoption (ownership, accountability, approval thresholds, and escalation paths), ensuring consistent governance across business lines and functions
- Embed AI governance into existing delivery and operational processes (e.g., vendor management, model risk/validation, SDLC, change management, incident management, privacy/security reviews) so governance is executed through operating rhythms, not standalone compliance activity
- Establish and run AI risk assessment, audits, and impact analysis processes to identify, document, and mitigate ethical, regulatory, operational, and reputational risks (including fairness, transparency, privacy, explainability, data lineage/quality, and lifecycle oversight)
- Provide enterprise visibility and reporting on AI inventory, risk posture, control effectiveness, performance and drift, operational stability, issues/incidents, and remediation progress delivering actionable insights and recommendations to senior leadership and governance forums
- Partner cross\-functionally (Business, IT, Data, Risk, Compliance, Legal, Security, HR) to ensure AI solutions are designed and operated responsibly, with clear requirements, controls, and accountability for outcomes
- Evaluate AI use cases and vendor solutions for readiness (governance, risk, control maturity, implementation feasibility, monitoring capability, documentation quality) and provide go/no\-go and risk acceptance recommendations
- In partnership with the People Team, enable responsible adoption through guidance and education by creating practical standards, templates, training, and “how\-to” support that helps teams implement controls correctly and efficiently
Other
- Adheres to all company policies, procedures, and business ethics codes
- Completes required regulatory training as assigned
- Maintains strict adherence to and compliance with all laws, rules, regulations, and internal controls specific to the role, including but not limited to Bank Secrecy Act, Anti\-Money Laundering, USA Patriot Act, OFAC and Fair Lending regulations
Knowledge/Skills/Abilities
- Enterprise Responsible AI / AI risk management expertise: demonstrated experience designing and implementing Responsible AI, AI governance, and/or data/model governance frameworks in complex organizations
- Strong command of risk and controls for AI across build and buy: ability to assess and manage risks spanning data privacy, security, bias/fairness, transparency/explainability, third‑party risk, auditability, model performance, drift, and operational resilience
- Regulatory and standards awareness with translation to practice: proven ability to stay current on evolving requirements and convert them into clear policies, control objectives, procedures, and measurable control tests
- Operationalization mindset: experience embedding governance into SDLC, MRM/validation, vendor management, change management, and ongoing monitoring, moving from principles to repeatable execution
- Assessment capability: ability to perform and/or lead AI risk assessments, impact analyses, control design reviews, and evidence\-based evaluations; develop remediation plans and track issues to closure
- Influence and communication: ability to drive alignment across functions, facilitate decision forums, and communicate complex AI risk topics to both technical teams and senior leadership with clear recommendations
- Analytical rigor and structured problem\-solving: ability to translate complex requirements into actionable controls, operating models, and reporting that supports consistent enterprise decision\-making
Core Competencies
- Demonstrating Member Obsession
- Puts themselves in the Member’s shoes
- Looks for friction points
- Makes it personalized and easy
- Demonstrating Performance Excellence
- Sets standards for elevating excellence
- Ensures elevated quality
- Takes responsibility
- Conducts continuous improvement
- Demonstrating Innovation
- Challenges current thinking
- Approaches change with a positive mindset
Experience
Minimum Requirements
- Bachelor’s degree in a relevant field such as Data Science, Computer Science, Statistics, Risk Management, Information Systems, or related discipline, or equivalent combination of education and experience
- Technical, risk/compliance, or policy background, with demonstrated experience applying governance, ethical, and regulatory principles to AI or data\-driven system
- 5\-7 years’ experience in AI governance, data ethics, risk management, compliance, or related roles in a technology\-driven environment
- Deep understanding of AI/ML technologies, ethical and regulatory challenges, and responsible AI principles
- Strong communication and stakeholder management skills, with the ability to engage technical and non\-technical audiences
- Project management experience, with a track record of delivering cross\-functional initiatives
- Knowledge of relevant legal and regulatory frameworks for AI (e.g., GDPR, EU AI Act)
- Must be bondable
Preferred Requirements
- Proven experience developing and implementing ethics, compliance, or governance programs within a complex organization
- Experience in highly regulated industries such as financial services, insurance, or healthcare
- Certifications or advanced training in AI ethics, risk management, or compliance
- Demonstrated thought leadership or public engagement in responsible AI or digital ethics
Physical Demands
The physical demands described are representative of those that must be met by an employee, with or without accommodation, to successfully perform the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.
Frequent
- While performing the duties of this job, the employee is regularly required to sit; use hands to finger, handle or feel; reach with hands and arms; and talk or hear.
- Specific vision abilities required by this job include close vision, distance vision, peripheral vision, and ability to adjust focus.
- Employee will make extensive use of the telephone and virtual communications requiring the ability to explain complex information effectively and accurately.
Work Environment
The work environment characteristics described are representative of those an employee encounters while performing the essential functions of this job.
- This position requires frequently working onsite at UFCU Plaza in Austin, Texas.
- This position may involve periodic stressful conditions.
- May occasionally require an adjusted work schedule, overtime, and evening/weekend hours.
- May occasionally move from one work location/branch to another.
- Public contact position, requiring appropriate professional appearance.
- Frequent computer use at a workstation of up to two hours at a time.
- The noise level in the work environment is usually moderate.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,708 AI roles we're tracking, AI Safety positions make up 0% of the market. At University Federal Credit Union, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills in Demand for This Role
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Safety roles pay a median of $300,000 based on 21 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
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 Research Engineer ($280,000) and AI Architect ($254,798). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
University Federal Credit Union AI Hiring
University Federal Credit Union has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Safety. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into AI Safety roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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