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Position Type :
Full timeType Of Hire :
Experienced (relevant combo of work and education)Job Description
About FIS
FIS is a global leader in financial services technology, providing software, services, and data\-driven solutions that power banks, capital markets firms, and businesses of all sizes around the world. The company's platforms span core banking, payments, capital markets, and banking\-as\-a\-service, processing trillions of dollars in transactions annually and supporting thousands of financial institutions and businesses across more than 100 countries. Headquartered in Jacksonville, Florida, FIS is publicly traded and is regularly recognized as one of the largest fintech companies globally, combining deep financial services domain expertise with modern technology to help clients run mission\-critical operations, manage risk, and grow in an increasingly complex regulatory environment.
The Opportunity
FIS is seeking a Vice President, Compliance Artificial Intelligence (AI) \& Innovation, to lead the function's oversight of AI, technology \& data governance and transformation of compliance technology systems and processes across the enterprise. This is a newly defined leadership role reporting to the Chief Compliance Officer (CCO), created as FIS continues embedding AI across its products, platforms, and internal operations while strengthening governance, validation, monitoring, and regulatory readiness. The successful candidate will be a hands\-on, credible leader capable of building practical compliance oversight for AI and data\-enabled capabilities for compliance and financial crime solutions while partnering closely with leaders across Product, Technology, Risk, Compliance and Legal to drive responsible innovation at enterprise scale.
This role is based at FIS's headquarters in Jacksonville, Florida.
What You Will Do
- Lead the Compliance function’s AI technology and data strategy, including engagement with cross\-functional FIS AI teams, and standards for Compliance department AI tools and data use.
- Work cross\-functionally to develop and maintain compliance frameworks, policies, and standards specific to AI and data governance, ensuring alignment with evolving regulatory guidance (e.g., EU AI Act, NIST AI RMF, US federal and state AI regulations, and financial services regulatory expectations).
- Represent Compliance or act as the CCO’s delegate in enterprise\-wide AI governance meetings, working groups, steering committees, and other forums that require Compliance input.
- Establish and operate ongoing AI compliance monitoring programs, including issue escalation, testing cadences, control monitoring, and reporting to enterprise risk and compliance committees, including working with other compliance leaders to ensure AI, data governance, and compliance technology requirements are integrated consistently across the broader compliance program.
- Serve as a subject\-matter authority on AI governance for Compliance, regulatory developments, and industry best practices affecting financial technology providers.
- Oversee AI enablement and automation within Compliance by partnering with compliance leaders to identify and implement opportunities that improve the function’s efficiency, consistence, and effectiveness.
- Advise business and technology leaders on the compliance implications of new AI\-enabled products, internal tooling, and data\-driven capabilities prior to launch.
- Build and lead a team of compliance technology and data professionals, developing the operating model, capabilities, and governance routines required to support a large, complex, highly regulated fintech.
- Coordinate and contribute to compliance thought leadership in priority and emerging areas, including AI, digital assets, and fraud, ensuring FIS maintains a forward\-looking compliance posture, working with Policy and Regulatory Legal teams as appropriate.
What We're Looking For
- 10\+ years of progressive experience in compliance, risk management, technology and data governance, or related fields, ideally within financial services, payments, banking, or financial technology.
- Meaningful exposure to AI/ML systems, data governance, and compliance technology in a regulated environment.
- Working knowledge of applicable regulatory frameworks governing AI, financial crimes compliance, regulatory compliance, or similar standards as applicable.
- Demonstrated experience building, scaling, or modernizing a compliance monitoring, validation, testing, or governance function within a large, matrixed organization.
- Strong ability to translate technical AI, data, and model governance concepts for non\-technical executive, legal, compliance, and regulatory audiences.
- Proven people\-leadership experience, with the credibility to operate as a trusted advisor to senior leadership across the business and corporate functions.
- Bachelor's degree required; advanced degree such as a J.D., MBA, or other relevant graduate degree preferred.
- Willingness to be based in or relocate to Jacksonville, Florida.
What Sets a Strong Candidate Apart
FIS is particularly interested in candidates who combine regulatory and compliance discipline with practical experience governing technology\-enabled business transformation. Strong candidates will bring hands\-on experience with AI governance and compliance technology modernization, along with the ability to partner credibly with both technical teams and senior legal, compliance, risk, and business leaders.
Candidates who have built or evolved AI, data, model risk, or compliance technology oversight capabilities in a regulated financial services or fintech environment are especially encouraged to apply.
Privacy Statement
FIS is committed to protecting the privacy and security of all personal information that we process in order to provide services to our clients. For specific information on how FIS protects personal information online, please see the Online Privacy Notice.
EEOC Statement
FIS is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, marital status, genetic information, national origin, disability, veteran status, and other protected characteristics. The EEO is the Law poster is available here supplement document available here
For positions located in the US, the following conditions apply. If you are made a conditional offer of employment, you will be required to undergo a drug test. ADA Disclaimer: In developing this job description care was taken to include all competencies needed to successfully perform in this position. However, for Americans with Disabilities Act (ADA) purposes, the essential functions of the job may or may not have been described for purposes of ADA reasonable accommodation. All reasonable accommodation requests will be reviewed and evaluated on a case\-by\-case basis.
Sourcing Model
Recruitment at FIS works primarily on a direct sourcing model; a relatively small portion of our hiring is through recruitment agencies. FIS does not accept resumes from recruitment agencies which are not on the preferred supplier list and is not responsible for any related fees for resumes submitted to job postings, our employees, or any other part of our company.
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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 FIS, 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 $218,750 based on 3,817 positions with disclosed compensation.
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.
FIS AI Hiring
FIS has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Jacksonville, FL, US.
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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