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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: A Strategic Leadership Role at the Intersection of Law, Data \& AI
FIS is seeking a Senior Vice President, Deputy General Counsel, Product, Data \& AI Strategy, Technology to support the company's product, data and artificial intelligence and technology initiatives during a period in which AI and data\-enabled capabilities are becoming increasingly central to FIS's product strategy, client solutions, and enterprise operating model. This is a highly visible role reporting to the Chief Legal Officer, with regular partnership across product, technology, data, compliance, risk, commercial, and executive leadership. The successful candidate will be a hands\-on, commercially practical legal leader capable of providing senior counsel across product development, data and AI strategy, technology, intellectual property, and emerging regulatory requirements while helping FIS scale responsible innovation across a complex, global fintech environment.
Reporting directly to the Chief Legal Officer, this executive will partner regularly with FIS’s product, technology, data, compliance, risk, and commercial leadership teams and will serve as a trusted advisor to the C\-suite on matters of data strategy, AI governance, and emerging technology risk. The successful candidate will bring deep product counsel experience, a sophisticated understanding of data and AI technologies, and the judgment and presence to operate as a senior member of the Office of the Chief Legal Officer. This is a business\-embedded counsel position for a legal leader who thrives at the intersection of innovation, risk, and commercial pragmatism.
What You Will Do
- Data \& AI Product Counsel Leadership. Serve as FIS’s senior product counsel for data and AI, providing strategic legal guidance across the full product development lifecycle, from ideation, design, and build through launch, iteration, and post\-deployment monitoring. Act as the legal voice in product roadmap discussions and ensure that data and AI considerations are embedded in product strategy from the outset.
- AI \& Machine Learning Advisory. Advise product, engineering, and data science teams on the legal risks, requirements, and governance frameworks associated with developing, licensing, training, and deploying AI and machine\-learning capabilities within FIS products. Provide counsel on model development, data inputs, algorithmic decision\-making, explainability, and responsible AI principles.
- Regulatory Intelligence \& Policy Leadership. Track, interpret, and advise on the rapidly evolving regulatory landscape for AI and data use in financial services, including U.S. federal and state AI regulation, international frameworks (e.g., EU AI Act), and sector\-specific guidance from banking, payments, and capital markets regulators. Serve as FIS’s internal thought leader on legal and policy developments affecting AI and data\-driven products.
- AI Governance \& Enterprise Risk Partnership. Partner with legal, compliance, enterprise risk, technology, and information security leadership to develop, implement, and mature FIS’s AI governance framework. Ensure alignment between legal, compliance, and risk perspectives on AI risk management, model governance, and responsible innovation.
- Commercial \& Transactional Counsel. Structure, review, and negotiate complex data\- and AI\-related commercial agreements—including technology vendor contracts, data licensing arrangements, AI platform agreements, strategic partnerships, and client\-facing terms governing AI\-enabled products and services.
- Executive Advisory \& Strategic Influence. Serve as a senior legal advisor to executive leadership on data, AI, and technology strategy. Represent the Office of the Chief Legal Officer in cross\-functional product, technology, and governance forums. Translate complex legal and regulatory issues into actionable, business\-oriented guidance for the C\-suite and Board as needed.
- Team Leadership \& Practice Development. Build, lead, mentor, and scale a specialized team of product counsel supporting FIS's data, AI, and technology portfolio. Establish the legal team’s reputation as a trusted, proactive partner to the business and drive continuous development of the data/AI product counsel function as a strategic capability within FIS Legal.
- APAC Team Oversight. Serve as the direct manager of the Head of Legal India, providing leadership, strategic direction, and oversight of FIS’s APAC\-based legal operations.
What We're Looking For
- J.D. and active bar membership in good standing, with 15\+ years of relevant legal experience, including significant in\-house experience advising on technology, data, AI, or product counsel matters.
- Deep, demonstrated experience serving as product counsel for AI, machine learning, data\-intensive, or technology\-enabled products, ideally within financial services, fintech, payments, banking, capital markets, or a leading technology company operating at scale.
- Strong command of the legal and regulatory frameworks governing data use, AI, and emerging technology, including data privacy (e.g., CCPA, GDPR), intellectual property, commercial contracting, and AI\-specific regulation applicable to financial services.
- Experience advising senior executives and cross\-functional product, engineering, data, compliance, risk, and technology leadership in a fast\-paced, matrixed environment.
- Excellent judgment and the ability to balance legal risk management with commercial pragmatism and the pace of product innovation.
- A track record of building trusted relationships with business and technology stakeholders while maintaining legal independence and sound governance discipline.
- Prior people\-leadership experience, with the gravitas to operate as a senior member of the Office of the Chief Legal Officer.
- Willingness to be based in or relocate to Jacksonville, Florida.
What Sets a Strong Candidate Apart
FIS is seeking a legal leader who combines elite product counsel skills with deep fluency in data, AI, and emerging technology. The ideal candidate will have operated as a strategic partner to product and engineering teams, shaped the legal architecture for AI\-enabled or data\-intensive products, and translated complex technical, legal, and regulatory issues into clear, actionable guidance for senior business leaders. This person brings intellectual curiosity about technology, a commercial mindset, and the leadership presence to influence at the highest levels of a large, complex, global organization.
Candidates who have built, led, or matured a dedicated data, AI, or technology product counsel function, particularly in a regulated financial services, fintech, payments, or global technology 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
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, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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