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About This Role
Available in 3 locationsAvailable in 3 locations
AI Security Architecture and Risk, Sr Advisor
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Available in 3 locations
Alpharetta, Georgia, United States of America
Columbus, Ohio, United States of America
Hagerstown, Maryland, United States of America
Job ID: R\-10398809
Category: Other
Onsite
Posting Start Date: Posting Start Date: 30\-July\-2026
Posting End Date: Posting End Date: 17\-August\-2026
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
AI Security Architecture and Risk, Sr AdvisorAbout your role
You will lead the security design and risk\-assessment approach for the enterprise AI program at Fiserv, shaping the reference architectures, risk tiering, and guardrail standards that let the business adopt AI quickly and safely. Sitting within Cybersecurity, this role helps lead a cross\-functional model for AI security that connects the strengths of Cyber, the AI Center of Excellence, Cybersecurity Architecture, Risk, and the business units. It is a cornerstone of the AI Security Protection team and a driver of the Fiserv transformation to secure\-by\-design AI delivery.
What you’ll do
- Help lead the enterprise AI security reference architecture — partnering with Cybersecurity Architecture and the AI Center of Excellence to define approved patterns and secure defaults for AI and agentic workloads, and to evaluate incoming designs against them.
- Help build and steward the AI risk\-assessment framework — a multi\-factor risk\-tiering model that routes initiatives into delivery lanes (Standard, Elevated Risk, Experimentation) with clear, auditable criteria for rapid, consistent decisions at scale.
- Define the control requirements and thresholds that AI protection tooling enforces — guardrail thresholds for hallucination, PII/PHI exposure, prompt injection, and toxicity, plus data\-access policy — and bring security leadership to the build\-vs\-buy strategy for that layer (e.g., Arize, Fiddler, Varonis, or equivalent).
- Develop and maintain STRIDE, MAESTRO, and MITRE ATLAS threat models for new AI system patterns, and contribute the Architecture Decision Records that keep the program defensible to internal auditors and regulators.
- Represent Cyber's AI security perspective in AI Center of Excellence governance, and help translate design decisions into platform\-enforceable policy and guardrails.
- Evaluate emerging agentic AI frameworks (multi\-agent orchestration, LLM tool\-calling, autonomous workflows) for architectural risk and help define secure defaults with partner teams before they reach production.
- Lay the groundwork for a future automation layer by designing controls and decision logic so they can be progressively operated by AI agents as the program matures.
- Responsibilities listed are not intended to be all\-inclusive and may be modified as necessary.
Experience you’ll need to have
- 10\+ years in security architecture, with a focus on cloud\-native, AI/ML, or platform security domains.
- Hands\-on proficiency with AI risk frameworks: NIST AI RMF, MITRE ATLAS, OWASP LLM Top 10, and ISO/IEC 42001, MAESTRO.
- Demonstrated experience designing risk\-classification or governance models that balance security assurance against delivery speed.
- Experience with policy\-as\-code tooling (Sentinel) and translating security policy into platform\-enforceable controls.
- Familiarity with the commercial AI protection landscape — observability, guardrails, and data security posture management — sufficient to make credible build\-vs\-buy and tooling recommendations.
- A track record of leading through influence across organizational boundaries, building alignment with governance, platform, and business teams to deliver shared outcomes.
- Strong technical communication skills; proven ability to present architecture and risk positions to SVP and C\-suite audiences.
- Bachelor's degree in Computer Science, Cybersecurity, or a related field, and/or equivalent work experience.
Experience that would be great to have
- Experience in financial services or other regulated industries with AI governance under frameworks such as the EU AI Act, ISO/IEC 42001, or model risk guidance such as SR 11\-7\.
- Hands\-on experience evaluating LLM\-based agent orchestration systems (LangGraph, AutoGen, CrewAI, or equivalent) in a security or governance context.
- Experience helping shape cross\-functional operating models as organizations scale new technology capabilities.
- Certification in CISSP, CISM, or equivalent; advanced credentials in AI, cloud, or zero trust security.
- Proven experience contributing to enterprise\-level threat modeling programs with patterns adopted by engineering teams.
How you’ll work
- This role is on\-site Monday through Friday. Fiserv considers in\-person collaboration to be an essential part of this role as in\-person office experiences help you with your overall onboarding experience and leads to stronger productivity.
Travel
- Approximately 10% travel off\-site or to other office locations is expected.
Sponsorship
- You must currently possess valid and unrestricted U.S. work authorization to be considered for this role. Individuals with temporary visas including, but not limited to, F\-1 (OPT, CPT, STEM), H\-1B, H\-2, or TN, or any candidate requiring sponsorship, now or in the future, will not be considered.
Salary Range:
$103,000\.00\-$144,000\.00
These pay ranges apply to employees in Maryland. Pay ranges for employees in other states may differ. Certain Positions are Commissions eligible.
It is unlawful to discriminate against a prospective employee due to the individual's status as a veteran.
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 $103K-$144K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($123K) sits 43% below the category median. Disclosed range: $103K to $144K.
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.
Fiserv AI Hiring
Fiserv has 10 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Sunnyvale, CA, US, Omaha, NE, US, Alpharetta, GA, US. Compensation range: $144K - $285K.
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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