Interested in this AI Engineering Manager role at Global Payments?
Apply Now →Skills & Technologies
About This Role
Every day, Global Payments makes it possible for millions of people to move money between buyers and sellers using our payments solutions for credit, debit, prepaid and merchant services. Our worldwide team helps over 3 million companies, more than 1,300 financial institutions and over 600 million cardholders grow with confidence and achieve amazing results. We are driven by our passion for success and we are proud to deliver best\-in\-class payment technology and software solutions. Join our dynamic team and make your mark on the payments technology landscape of tomorrow.
This AI software engineering leadership role is pivotal in delivering strategic Generative AI (GenAI) and Machine Learning (ML) initiatives that will transform Global Payments. As the Manager of AI Engineering, you will lead the strategic integration of artificial intelligence within the business context, driving strategic innovation and transformation. You will oversee the analysis of business processes, identify areas for AI enhancement, and design advanced AI solutions that align with our strategic business objectives. Your deep technical expertise in AI areas and business acumen will help bridging the gap between business vision and technical execution.
RESPONSIBILITIES
- Lead, mentor, and manage a team of software engineers, in building GenAI\-powered products, GenAI product components, and GenAI prototypes to help drive value for Global Payment in leveraging new GenAI technology to create value. This work will include assessing \& integrating GenAI into our existing products and systems to boost efficiency, personalize consumer experiences, and create entirely new offerings.
- Passionate about customer success with what your teams build. Take care to measure and monitor, that what your teams build is used, and useful to driving business outcomes
- Collaboratively power corporate and market usage of Enterprise\-hardened GenAI end\-user tools
- Capture opportunities and clarify business/customer problem statement, both for custom AI build\-out and 3rd party AI tool adoption
- Drive cross\-functional engagement/alignment as we move from idea to tech proof of concept, use case development, pilot, and deployment
- Manage resources and project timelines to ensure successful delivery of projects and deliverables
- Stay up\-to\-date with the latest developments in GenAI, and related areas, and leverage this knowledge to drive innovation
- Ensure the ethical use of AI, adhering to best practices and guidelines
- Facilitate internal COE communication and collaboration mechanisms
- Develop and implement corporate communication strategies to increase awareness and understanding of generative AI capabilities and updates across Global Payments
- Build and nurture an internal community of GenAI enthusiasts and practitioners, including internal platforms for employees to share GenAI experiences and ideas
- Organize regular internal and external/industry GenAI\-focused events and discussions
- Lead by example in adopting innovative practices in corporate communication
- Inspire and motivate your team, and foster a positive and productive work environment consistent with Global Payment’s values
MUST HAVES:
- Strategic thinker with strong intuition and first principles thinking around business performance, business drivers, and overall competitive landscape
- Strong project leadership and ability to manage multiple complex and ambiguous priorities across many teams and stakeholders
- Hands on engineering experience with production ready Gen AI \& ML application. At least 6 years of experience in software, including building enterprise\-grade systems. Should include several years leading an engineering team and work with AI/ML
- Expert on industry trends and various LLMs, including LLM Application development tools \& frameworks
- Excellent communication and storytelling skills
- Passionate engineering leader with experience building high performance teams, must lead and manage a team of software engineers, that includes backend, frontend, some design, etc
- At least bachelor’s or Master's degree in Computer Science, AI, or a related area
- Proficiency in stakeholder management to effectively communicate and manage expectations of those linked to the work outside your team
- Experience in strategic planning and execution with strong decision\-making skills to align initiatives with business goals and make informed choices that benefit the organization
- Some experience in handling compliance and regulatory requirements to ensure engineering practices adhere to relevant laws and regulations
BONUS ATTRIBUTES:
- Experience with Natural Language Processing (NLP) and building conversational AI agents.
- Familiarity with big data technologies (Apache Spark, Kafka)
- Knowledge of data governance and data security best practices.
- Experience with CI/CD tools and automation for AI/ML workflows.
- Knowledge in Snowflake and AWS/GCP cloud technologies.
- Understanding of reinforcement learning and its application in agentic AI.
- Experience with real\-time data processing and streaming analytics.
- Publications or contributions to open\-source projects in the AI/ML field.
ABILITIES:
- Ability to work proactively with a high level of initiative and accuracy.
- Ability to manage multiple assignments effectively and meet established deadlines.
- Strong interpersonal skills to interact professionally with staff and stakeholders.
- Excellent organizational skills and attention to detail.
- Critical thinking ability ranging from moderately to highly complex tasks.
- Flexibility in adapting to changing business needs and priorities.
- Ability to work creatively and independently with minimal supervision.
- Ability to utilize experience and judgment in accomplishing goals.
- Experience in navigating organizational structures and collaborating across teams.
What makes a Globalpayer?
Globalpayers think like a client, act like an owner and win as one team. We’re curious and innovative – always finding better ways to deliver impact. We empower each other to make decisions, and it’s our passion that drives excellence in everything we set out to do.
Does this sound like you? Then you sound like a Globalpayer. Apply now to take your career global. https://jobs.globalpayments.com/en/why\-global\-payments/benefits/
Applicant must be authorized to work in the U.S. without the need for employment\-based visa sponsorship now or in the future; We will not sponsor applicants for U.S. work visa status for this opportunity (no sponsorship is available for H\-1B, L\-1, TN, O\-1, E\-3, H\-1B1, F\-1, J\-1, OPT, CPT or any other employment\-based visa).
Diversity and EEO Statements
Global Payments is an organization that stands against racism, intolerance and injustice in all its forms — one that respects, honors and celebrates the diversity of our team members and the differences among us. Our commitment to fostering a company culture that values and respects Inclusion and Diversity is steadfast. Standing together as one company, we will continue to work to drive positive change for the communities in which we live and work and stamp out injustice. linkedin.com/in/shonali\-r\-66744622 \-SB
payments/benefits/
Global Payments Inc. is an equal opportunity employer. Global Payments provides equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex (including pregnancy), national origin, ancestry, age, marital status, sexual orientation, gender identity or expression, disability, veteran status, genetic information or any other basis protected by law. If you wish to request reasonable accommodations related to applying for employment or provide feedback about the accessibility of this website, please contact [email protected].
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 4,317 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At Global Payments, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Engineering Manager roles pay a median of $244,000 based on 23 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
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.
Global Payments AI Hiring
Global Payments has 1 open AI role right now. They're hiring across AI Engineering Manager. Based in Alpharetta, GA, US.
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 Engineering Manager roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.