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About This Role
At Jack Henry, we’re more than a technology company, we’re a force for good in financial services. We’re redefining how community banks and credit unions connect with the people they serve. Our mission is rooted in people inspired innovation, empowering financial institutions to deliver seamless, secure, and human centered experiences. We deliver cutting-edge solutions that are paving the way for the next generation of digital banking and payments, but our true impact begins with our associates. If you're ready to help transform an industry and grow with a company that values purpose, collaboration, and excellence then we’d love to meet you.
This Senior AI Engineer bridges AI experimentation and production engineering. This role is responsible for turning AI prototypes, agents, and platform capabilities into reliable, secure, enterprise-ready solutions. Working closely with engineering, cloud infrastructure, cybersecurity, and AI enablement teams, they operationalize AI systems that can scale across Jack Henry while meeting compliance and governance requirements.* AI Platform Engineering: Build and support production AI capabilities including AI Gateway services, MCP integrations, agent frameworks, self-hosted inference, and AI-powered applications.
- Production Readiness & Reliability: Develop testing, evaluation, monitoring, logging, and observability practices that ensure AI systems remain dependable, scalable, and auditable in production.
- Cross-Functional Delivery: Partner with cybersecurity, cloud infrastructure, architecture, and business teams to design and deliver AI solutions that meet enterprise standards.
- AI Optimization & Governance: Improve AI system performance through model evaluation, operational monitoring, quality assurance, and continuous improvement practices.
- Technical Leadership & Adoption: Mentor engineers, establish best practices for AI development, and help drive adoption of AI technologies and agentic development workflows across the organization.
This position is a hybrid role requiring at least 1 day per week in any of the following office locations: Allen, TX, Louisville, KY, Birmingham, AL, Cedar Falls, IA, Charlotte, NC, Overland Park, KS, Monett, MO or Springfield, MO.
This position is ineligible for immigration sponsorship and support. Please do not apply if at any time you will need immigration support now or in the future (i.e., H-1B, PERM). What you'll be responsible for:* Performs and leads system analysis and programming activities that take AI proofs of concept to production-ready software — including the research needed where no established pattern exists yet, and timely, quality delivery into production.
- Performs, and may lead, technical/engineering support for new and existing applications from code delivery until the retirement of the application.
- Develops, tests and reviews applications based on business requirements and industry best practices. Uses best practices, standard guidance, and agentic development workflows to create and modify code.
- Considers how code changes will affect end users and internal teams.
- Reviews code created by less experienced team members.
- Collaborates with quality and security partners — cybersecurity, cloud infrastructure, and AI governance — to ensure timely delivery of high-quality products.
- Performs and may lead work with cross-team as projects dictate.
- Works on and leads critical tasks and deliverables with no assistance, and sets and updates expectations on size, scope, and timelines.
- Participates in and may lead team in cross-functional meetings and discussions.
- Creates required technical documentation.
- Stays updated with emerging AI technologies and industry trends, recommending innovative solutions to improve software development processes.
- Adheres to documented departmental and corporate standards.
- Participates in the definition and documentation of standards — including what "production-ready" means for AI systems at Jack Henry.
- Lead unit tests, integration tests, and evals to ensure the reliability, security, and performance of developed software, especially AI systems and workflows.
- Debugs and troubleshoots issues as they arise, providing timely resolutions — including failure modes specific to AI systems, like quality drift, prompt regressions, provider outages, and cost spikes.
- May contribute to product architecture.
- May Perform other duties as assigned. What you'll need to have:* Must have a minimum of 6 years of technical experience in software development.
- Production ownership of AI systems (AI agents, AI features in products, MCP servers, etc).
- Experience with a major cloud environment (GCP, AWS, or Azure).
- Experience with unit testing and end-to-end automated testing.
- Experience with Kubernetes.
- Experience with any of the following: Large language models (LLM), vector databases.
- Comfort with ambiguity and shifting priorities.
- Must be self-directed, and willing to push through roadblocks rather than wait them out.
- Must be able to travel up to 10% to attend internal business meetings, training and/or professional conferences. What would be nice for you to have:* Bachelor’s degree.
- Fintech experience.
- Experience with GCP and infrastructure as code (Terraform).
- Experience in cloud security and DevOps.
- Experience with model fine-tuning, evaluation (e.g. controlling for bias), and validation.
- Experience with AI observability, evals, and model monitoring.
If you got this far, we hope you're feeling excited about this opportunity. Even if you don't feel you meet every single requirement on this positing, we still encourage you to apply. We're eager to meet motivated people who align with Jack Henry's mission and can contribute to our company in a variety of ways.
Why Jack Henry?
At Jack Henry, we pride ourselves through our motto of, "Do the right thing, do whatever it takes, and have fun." We recognize the value of our associates and believe much of our company's strength and success depends on their well-being.
We demonstrate our commitment by offering outstanding benefit programs to ensure the physical, mental & financial well-being of our people is always met.
Culture of Commitment
Ask our associates why they love Jack Henry, and many will tell you it is because our culture is exceptional. We do great things together. Rising to meet challenges and seeking opportunities is part of who we are as an organization. Our culture has helped us stay strong through challenging times and we credit our dedicated associates for our success. Visit our Corporate Responsibility site to learn more about our culture and commitment to our people, customers, community, environment, and shareholders.
Equal Employment Opportunity
At Jack Henry, we know we are better together. We value, respect, and protect the uniqueness each of us brings. Innovation flourishes by including all voices and makes our business - and our society - stronger. Jack Henry is an equal opportunity employer and we are committed to providing equal opportunity in all of our employment practices, including selection, hiring, performance management, promotion, transfer, compensation, benefits, education, training, social, and recreational activities to all persons regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, genetic information, pregnancy, marital status, sex, gender, gender identity, gender expression, age, sexual orientation, and military and veteran status, or any other protected status protected by local, state or federal law.
No one will be subject to, and Jack Henry prohibits, any form of discipline, reprisal, intimidation, or retaliation for good faith reports or complaints of discrimination of any kind, pursuing any discrimination claim, or cooperating in related investigations.
Requests for full corporate job descriptions may be requested through the interview process at any time.
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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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Jack Henry & Associates, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Jack Henry & Associates AI Hiring
Jack Henry & Associates has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Louisville, KY, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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