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Description Head of Agentic Payments
Leawood, KS preferred | Remote U.S. considered for an exceptional candidate Build the payment infrastructure behind AI-powered commerce
AI agents are creating new ways for people and businesses to purchase goods and services. Those transactions need trusted payment infrastructure: clear authorization, reliable money movement and rules for what happens when something goes wrong.
Euronet Worldwide is building that capability, drawing on its regulated licensing, global payout reach and stored-value infrastructure. With an initial pilot already underway with a major agentic marketplace, we’re seeking a Head of Agentic Payments to lead the launch and turn the first implementation into a scalable business.
You will own the product definition, partner relationships, launch execution and business economics. You’ll connect the capabilities of epay, Ren and Dandelion to deliver a service that additional platforms can adopt without rebuilding it for each customer.
This is an opportunity for an experienced payments builder who has brought products to market, navigated complex regulatory and commercial decisions, and wants direct accountability for a new business.
What You’ll Do
Define the product and roadmap
- Own the end-to-end payment capability, including issuance, authorization, settlement, payouts, refunds and disputes.
- Partner with Engineering to establish a reusable interface across Euronet platforms that supports efficient integration with new partners.
- Define payment permissions, transaction evidence, dispute rules and related terms with Legal and Compliance.
- Develop domestic and cross-border payout options, including ACH, local payment schemes and Dandelion.
- Build a roadmap from initial B2B use cases into consumer transactions, additional currencies and physical goods, with the appropriate regulatory path.
Bring the first partner to launch
- Own the day-to-day relationship with the pilot partner’s program, product and finance teams.
- Resolve outstanding commercial, funding, compliance and delivery requirements.
- Coordinate across Legal, Compliance, Treasury, Finance and Engineering, establishing clear ownership and escalating decisions when needed.
- Direct program coordination and provide leadership with accurate updates on progress, risks and launch readiness.
Build the partner ecosystem
- Develop a qualified pipeline of AI platforms, agent frameworks, commerce marketplaces and enterprise software companies enabling agent-initiated purchases.
- Negotiate partner arrangements that create a foundation for repeatable growth.
- Evaluate emerging protocols and standards, determining where Euronet should integrate, adapt or build.
- Represent Euronet in relevant industry forums and standards groups.
- Translate competitive developments into recommendations for product direction and market positioning.
Own the commercial model
- Build the business case with Finance, including pricing, transaction economics, working capital, settlement timing and operating costs.
- Develop competitive pricing for domestic and cross-border services.
- Present investment recommendations, growth opportunities and risks to senior leadership and the board.
- Develop the team and resource plan needed to scale the business.
What Success Looks Like
- First 90 days: Clear recommendations on outstanding commercial and funding decisions, a competitive landscape assessment and an actionable plan to resolve launch barriers.
- By six months: The first partner program launched—or a committed launch date with all prerequisites resolved—and agreement on the dispute framework and reusable platform interface.
- By nine months: A qualified partner pipeline with at least two additional platforms in active diligence, plus a roadmap for expanded use cases.
- By twelve months: A second platform integration funded and scoped, with an approved team plan to support growth.
Requirements What You’ll Bring
- 10+ years of payments experience, including substantial experience in regulated money movement, stored value, e-money or issuing.
- A demonstrated record of launching a payments product with a major platform partner, with ownership spanning commercial agreements, compliance and delivery.
- An established network across major payments companies, fintechs and leading AI companies.
- Experience driving results across Legal, Compliance, Treasury and Engineering without direct reporting authority.
- Strong understanding of payments economics, including transaction fees, interchange, float, working capital and settlement timing.
- The ability to engage credibly with partner product and finance leaders and internal compliance specialists.
- Clear, precise writing skills for product definitions, operating rules, business cases and negotiating positions.
- Sound judgment, hands-on execution and the ability to learn an emerging technical domain quickly.
- A direct communication style that surfaces risks early and helps leadership make informed decisions.
- Approximately 25% travel.
Preferred Experience
- Familiarity with agent payment protocols, including AP2, the Agentic Commerce Protocol, x402 and Model Context Protocol.
- Experience with stablecoins, tokenized deposits or programmable settlement.
- Marketplace or platform payouts, including cross-border disbursements.
- Participation in payments standards or scheme rulebook development.
- Building a partnership function or pipeline from the ground up.
Benefits
- 401(k) Plan
- Health/Dental/Vision Insurance
- Employee Stock Purchase Plan
- Company-paid Life Insurance
- Company-paid disability insurance
- Tuition Reimbursement
- Paid Time Off
- Paid Volunteer Days
- Paid Holidays
- Casual Office Attire
- Plus many more employee perks & incentives!
We are an Equal Opportunity Employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity, or national origin, age, disability status, genetic information, protected veteran status, or any other characteristic protected by law.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Euronet, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Euronet AI Hiring
Euronet has 1 open AI role right now. They're hiring across AI/ML Engineer.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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