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About This Role
Calling all innovators \- find your future at Fiserv.
We're Fiserv, a global leader in Fintech and payments, and we move money and information in a way that moves the world. We connect financial institutions, corporations, merchants and consumers to one another millions of times a day \- quickly, reliably, and securely. Any time you swipe your credit card, pay through a mobile app, or withdraw money from the bank, we're involved. If you want to make an impact on a global scale, come make a difference at Fiserv.
Job Title
Lead AI Transformation EngineerAbout Your Role
As a Lead AI Transformation Engineer, you will play a critical role in advancing AI\-driven engineering practices and establishing technical excellence across the Issuer Solutions portfolio. You will work closely with software development and mainframe teams across the organization to drive the adoption of AI\-assisted development tools and deliver solutions that align with the business goals and client needs.
What you’ll do:
- Set and evolve engineering standards (code quality, architecture patterns, CI/CD practices, security posture) across the Issuer Solutions portfolio
- Conduct solution architecture reviews, code reviews, and design consultations that raise the quality bar for teams building in Java/Spring, .NET, Angular, Python, and related technologies
- Define reusable patterns, reference implementations, and decision frameworks that teams can adopt independently
- Bridge the gap between corporate platform teams (AI CoE, Cloud Engineering, Security) and delivery teams by translating enterprise mandates into actionable delivery
- Champion the adoption and integration of AI\-assisted engineering workflows demonstrating measurable impact on quality and throughput
- Evaluate, pilot, and operationalize AI developer tools across the organization, coaching teams to integrate them into their daily workflows
- Identify systemic quality issues across the portfolio and propose durable, root\-cause solutions by building internal tools and contribute to critical\-path code when the situation demands it
- Deliver technical training and enablement sessions on AI adoption emerging AI\-assisted practices while mentoring senior engineers toward deeper architectural thinking, production ownership, AI fluency, and cross\-team influence
Experience you’ll need to have:
- 10\+ years of professional software engineering experience, with at least 5 years operating at a staff, principal, or architect level of influence across multiple teams
- 8\+ years of hands\-on experience across mainframe and distributed technologies, including Assembler, COBOL, and Java\-based enterprise applications, with the ability to bridge legacy and modern architectures
- 8\+ years of experience with CI/CD pipelines, DevOps, and AppOps practices, including production support, monitoring, incident management, and operational excellence
- 8\+ years of experience with cloud\-native and containerized environments (OpenShift, AWS/Azure/GCP), with exposure to hybrid mainframe\-cloud ecosystems
- 8\+ years of experience with modern databases (SQL, No\-SQL) and mainframe data stores where applicable
- 5\+ years of experience in modernizing and integrating mainframe systems with distributed platforms (microservices, APIs, event\-driven architectures)
- 3\+ years of experience adopting and championing AI\-assisted development tools to improve engineering outcomes at scale
- Bachelor’s degree in computer science, or a relevant field, or an equivalent combination of education, work, and/or military experience
Experience that would be great to have:
- Excellent written and verbal communication skills — able to produce clear technical documentation, present to senior leadership, and mentor engineers across platforms and domains
- Proven track record of driving end\-to\-end ownership from development through production operations, ensuring reliability, scalability, and performance
- Solid understanding of application security practices across both mainframe and distributed platforms (encryption, access control, secrets management, vulnerability remediation)
- Strong experience in AppOps, including performance tuning, resiliency engineering, batch processing, and production stability for mission\-critical systems
- Prior experience in the financial services industry
How you’ll work:
- Fiserv emphasizes in\-person collaboration to help you grow your career while shaping the future of fintech, this role is on\-site Monday through Friday
- This role requires use of a computer and audio equipment
Travel:
- Approximately 10% travel off\-site or to other office locations is expected
Sponsorship:
- In order to be considered, you must be legally authorized to work in the U.S. without need for visa sponsorship now or in the future
Salary Range
$186,000\.00 \- $285,600\.00*These pay ranges apply to employees in New Jersey and New York. Pay ranges for employees in other states may differ.*
It is unlawful to discriminate against a prospective employee due to the individual's status as a veteran.
For incentive eligible associates, the successful candidate is eligible for an annual incentive opportunity which may be delivered as a mix of cash bonus and equity awards in the Company’s sole discretion.
Thank you for considering employment with Fiserv. Please:
- Apply using your legal name
- Complete the step\-by\-step profile and attach your resume (either is acceptable, both are preferable).
Our commitment to Equal Opportunity:
Fiserv is proud to be an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, gender, gender identity, sexual orientation, age, disability, protected veteran status, or any other category protected by law.
If you have a disability and require a reasonable accommodation in completing a job application or otherwise participating in the overall hiring process, please contact [email protected]. Please note our AskHR representatives do not have visibility to your application status. Current associates who require a workplace accommodation should refer to Fiserv’s Disability Accommodation Policy for additional information.
Note to agencies:
Fiserv does not accept resume submissions from agencies outside of existing agreements. Please do not send resumes to Fiserv associates. Fiserv is not responsible for any fees associated with unsolicited resume submissions.
Warning about fake job posts:
Please be aware of fraudulent job postings that are not affiliated with Fiserv. Fraudulent job postings may be used by cyber criminals to target your personally identifiable information and/or to steal money or financial information. Any communications from a Fiserv representative will come from a legitimate Fiserv email address.
Salary Context
This $186K-$285K range is above the 75th percentile 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 Fiserv, 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 ($235K) sits 8% above the category median. Disclosed range: $186K to $285K.
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.
Fiserv AI Hiring
Fiserv has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Berkeley Heights, NJ, US. Compensation range: $285K - $285K.
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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