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About This Role
Available in 3 locationsAvailable in 3 locations
Director, AI \- Process Engineering
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Available in 3 locations
Alpharetta, Georgia, United States of America
Berkeley Heights, New Jersey, United States of America
Columbus, Ohio, United States of America
Job ID: R\-10398810
Category: Other
Onsite
Posting Start Date: Posting Start Date: 30\-July\-2026
Posting End Date: Posting End Date: 28\-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
Director, AI \- Process EngineeringAbout your role:
As a Senior Advisor, Software Engineering, you will design and scale AI\-driven cybersecurity platforms that strengthen enterprise defense capabilities across Fiserv. You will work across cybersecurity, cloud engineering, data engineering, application development, and enterprise architecture teams to deliver secure, production\-grade solutions on Google Cloud. Your work will help improve threat detection, automate security operations, and advance AI\-enabled cyber resilience across the organization.
What you'll do:
- Design, develop, and deploy AI\-powered cybersecurity capabilities for threat detection, anomaly identification, identity and access risk analysis, security operations automation, incident response orchestration, and cyber threat intelligence.
- Architect and implement multi\-agent and agentic AI frameworks that support enterprise cybersecurity use cases, including Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), embeddings, vector search, and knowledge retrieval systems.
- Lead the engineering and delivery of scalable, cloud\-native AI security platforms on Google Cloud using tools such as Vertex AI, Gemini, BigQuery, Pub/Sub, Dataflow, Cloud Run, Cloud Functions, and Google Kubernetes Engine (GKE).
- Integrate AI capabilities with enterprise cybersecurity platforms and services, including Identity and Access Management (IAM), Privileged Access Management (PAM), secrets management, Security Information and Event Management (SIEM), Security Orchestration, Automation and Response (SOAR), Governance, Risk, and Compliance (GRC), and security telemetry platforms.
- Establish engineering standards and lifecycle practices for AI platforms, including model training pipelines, automated deployment, monitoring, evaluation, drift detection, Infrastructure as Code, and continuous integration and continuous deployment (CI/CD) for machine learning workloads.
- Embed security, privacy, compliance, and responsible AI practices into platform design by applying Zero Trust principles, Secure\-by\-Design methods, and controls against prompt injection, adversarial machine learning threats, data poisoning, model manipulation, and unauthorized access.
- Partner with cross\-functional technical and business teams to translate cybersecurity and business requirements into scalable AI solutions, while influencing architecture, technical direction, and enterprise adoption of modern AI engineering practices.
- Responsibilities listed are not intended to be all\-inclusive and may be modified as necessary.
Experience you'll need to have:
- 10\+ years of experience in software engineering, building enterprise\-scale applications and platforms using Python and modern software development practices.
- 10\+ years of experience designing and operating cloud\-native solutions on Google Cloud Platform (GCP) or an equivalent cloud environment.
- 10\+ years of experience in machine learning, Generative AI, Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), embeddings, vector databases, APIs, microservices, and distributed systems.
- 10\+ years of experience with Kubernetes, Docker, CI/CD platforms, MLOps, AIOps, and Infrastructure as Code in production environments.
- 10\+ years of experience in cybersecurity engineering across Identity and Access Management (IAM), Privileged Access Management (PAM), Identity Governance Administration (IGA), secrets management, Zero Trust architecture, and cloud security.
- 10\+ years of experience with Security Information and Event Management (SIEM), Security Orchestration, Automation and Response (SOAR), threat detection and response, Security Operations Center (SOC) technologies, and security automation platforms.
- Bachelor’s degree or higher in Engineering, Computer Science, Artificial Intelligence, Cybersecurity, Information Security, Data Science, or a related field, or equivalent combination of education, related experience and/or military experience.
Experience that would be great to have:
- Master’s degree or Ph.D. in Engineering, Computer Science, Artificial Intelligence, Cybersecurity, Information Security, Data Science, or a related field, or equivalent combination of education, related experience and/or military experience.
- Experience designing and deploying AI agents and autonomous security systems in enterprise environments.
- Experience in financial services, fintech, payments, banking, or another highly regulated industry.
Google Cloud certifications such as Professional Machine Learning Engineer or Professional Cloud Architect, or equivalent certification experience.
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: This role requires occasional travel (up to 10%).
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 for this role.
Benefits at Fiserv:
- Fuel Your Life program to support your physical, financial, social, and emotional well\-being.
- Paid holidays and generous time away policies.
- No\-cost mental health support through Employee Assistance Programs.
- Living Proof program to recognize your peers’ extra effort with points redeemable for rewards.
- Eight Employee Resource Groups to foster a collaborative culture and expand your network.
- Unparalleled professional growth with training, development, and internal mobility opportunities.
- Medical, dental, vision, life, and disability insurance options available from day one.
- Retirement planning including 401k match and discounted shares with the Employee Stock Purchase Plan.
- Tuition assistance and reimbursement program.
- Paid parental and military leave.
Salary Range
$127,500\.00 \- $204,000\.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 $127K-$204K range is below the median 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. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($165K) sits 23% below the category median. Disclosed range: $127K to $204K.
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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