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About This Role
Location: Berkeley Heights, New Jersey, United States of America
Job ID: R\-10393149
Category: Technology
Onsite
Posting Start Date: Posting Start Date: 29\-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
AI Accelerated Senior Java EngineerAbout your role:
As an AI Accelerated Senior Java Engineer you will be an elite software builder who delivers high\-scale, enterprise\-ready systems at exceptional speed, at \~10x. This role is for a highly productive engineer who multiplies their individual output by integrating advanced Generative AI and Large Language Model (LLM) developer tools into their daily workflow. Far beyond writing standard application code, this builder builds production\-grade Minimum Viable Products (MVPs), designs intelligent agentic workflows, and architectures scalable backend software that leverages LLMs directly to solve complex business problems.
You will have a proven track record of shipping actual production\-scale products, moving rapidly past localized sandboxes or basic code prototypes, as well as an exceptional builder mindset focused on hyper\-efficiency, continuous tooling experimentation, and engineering velocity.
What you’ll do:
- High\-Velocity MVP Delivery: Move rapidly from design concepts to writing production\-ready code. Ship fully realized, resilient enterprise applications with speed and autonomy.
- AI\-Assisted Engineering: Seamlessly embed GenAI assistants into daily workflows to drastically accelerate code writing, refactoring, test generation, and deep debugging.
- LLM \& AI Application Building: Design, implement, and deploy application layers that integrate LLMs, retrieval\-augmented generation (RAG) pipelines, and intelligent agentic workflows.
- Enterprise Java Engineering: Architect and write clean, maintainable, and high\-performance Java code capable of processing concurrent, enterprise\-scale workloads.
- CI/CD \& Cloud Automation: Build and manage automated pipelines to package, test, and deploy applications continuously to cloud ecosystems.
- Architecture \& Database Integration: Model secure, low\-latency relational and non\-relational database layers optimized for rapid application execution.
- Performance Tuning \& Diagnostics: Conduct deep\-dive profiling, memory leak detection, and technical troubleshooting on high\-throughput backend services.
Experience you’ll need to have:
- 8\-10\+ years in professional software engineering, with a strong focus on core backend Java platforms.
- Execution: A proven track record of shipping actual production\-scale products, moving rapidly past localized sandboxes or basic code prototypes.
- Automation \& Testing: Expert proficiency in writing automated unit, integration, and performance tests (e.g., JUnit, Mockito, Testcontainers).
- Mindset: An exceptional builder mindset focused on hyper\-efficiency, continuous tooling experimentation, and engineering velocity.
- Advanced, hands\-on engineering using Java 17\+ or Java 21\+ features (e.g., Virtual Threads, Pattern Matching).
- Deep expertise with Spring Boot, Quarkus, or Micronaut frameworks for building cloud\-native microservices.
- Developer Velocity: Demonstrated mastery using GenAI developer assistants (e.g., GitHub Copilot, Claude Code, etc.) to achieve "10x" development, documentation, and unit test coverage.
- LLM Engineering: Hands\-on integration with LLM APIs (OpenAI, Anthropic, or Amazon Bedrock) and vector databases.
- AI Frameworks: Building RAG and agent patterns using orchestration toolkits like Spring AI.
- Data Layers: Practical experience with PostgreSQL, NoSQL stores, and connection optimization.
- Streaming/Messaging: Experience with high\-throughput message brokers like Apache Kafka or AWS SQS/SNS.
- Deployment: Familiarity with Docker, Kubernetes, and major cloud environments preferably AWS.
- Bachelor’s degree in Computer Science, Information Technology, Information Systems, or a related field (or equivalent industry experience).
Experience that would be great to have:
- Experience in fintech/payments domains.
Important information about this role:
- 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.
- 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.
- This is a full\-time, direct\-hire position, and no contract options for unsolicited agency submissions will be considered.
- All offers of employment are contingent on standard background checks. Fiserv and certain of its affiliated companies are federal, state, and/or local government contractors. Should this position support a Federal Government contract, now or in the future, the successful candidate will be subject to a background check conducted by the U.S. Government to determine eligibility and suitability for federal contract employment for public trust or sensitive positions. Positions that support state and/or local contracts also may require additional background checks to determine eligibility and suitability.
\#LI\-MK1
Salary Range
$110,000\.00 \- $186,000\.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 $110K-$186K 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($148K) sits 31% below the category median. Disclosed range: $110K to $186K.
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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