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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.
Ready to take your career global?
Make your mark at one of the biggest names in payments. We are seeking a highly skilled and forward\-thinking AI Engineer to join our AI Engineering team. This role is ideal for a hands\-on technologist with deep expertise in machine learning (ML) and deep learning (DL), and a strong working knowledge of Generative AI technologies. You will design, build, and optimize intelligent systems that power automation, personalization, and decision\-making across our FinTech platforms. This is a unique opportunity to work at the intersection of cutting\-edge AI research and real\-world enterprise applications.
What you’ll own
- Design, develop, and deploy ML, DL, and Generative AI models that solve complex business problems and deliver measurable value.
- Build and maintain scalable ML pipelines and agentic workflows using modern frameworks and cloud\-native tools.
- Collaborate with data scientists, product managers, and engineers to translate business requirements into AI\-powered solutions.
- Implement and optimize AI solutions using platforms such as GCP Vertex AI, AWS Bedrock/SageMaker, and Snowflake Cortex.
- Apply techniques such as prompt engineering, RAG (Retrieval\-Augmented Generation), fine\-tuning, and RLHF to enhance model performance.
- Develop and deploy autonomous AI agents using frameworks like LangChain, LangGraph, and AgentSpace.
- Integrate vector databases (e.g., PGVector) and LLM orchestration tools to support retrieval and memory in generative systems.
- Ensure robust MLOps practices including CI/CD, monitoring, versioning, and lifecycle management of models.
- Stay current with AI research and industry trends, and evaluate emerging tools and techniques for enterprise adoption.
- Contribute to internal knowledge sharing, documentation, and best practices for responsible and ethical AI development.
What you’ll bring
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 4\+ years of experience in AI/ML engineering, with a strong foundation in ML/DL algorithms and systems.
- Proficiency in Python and ML libraries such as TensorFlow, PyTorch, and Transformers.
- Hands\-on experience with Generative AI models (e.g., GPT, Mistral, Claude) and agentic AI systems.
- Experience with NLP, conversational AI, and LLM\-based applications.
- Familiarity with vector search and semantic retrieval technologies.
- Strong understanding of MLOps, including model deployment, monitoring, and retraining.
- Experience building production\-grade AI systems at scale in cloud environments.
- Hands\-on experience with development on Google Cloud Platform.
- Excellent problem\-solving, communication, and collaboration skills.
It’s a bonus if you have
- Experience with prompt engineering, RLHF, and LLM evaluation techniques.
- Understanding of AI governance, safety, and responsible AI principles.
- Familiarity with reinforcement learning, multi\-agent systems, and autonomous workflows.
- Experience with big data technologies (e.g., Apache Spark, Kafka) and real\-time data processing.
- Experience with data engineering techniques and data warehousing platforms (e.g. BigQuery, Snowflake).
- Contributions to open\-source AI projects or publications in the AI/ML field.Familiarity with CI/CD pipelines, infrastructure\-as\-code, and cloud\-native AI tooling.
- 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.
About the team
Our inclusive and global teams win together every day. We’re proud to have the best minds in the industry, who you can learn from as you grow your career. The people, the energy, the connections – it’s unmatched. Come and be part of an ever\-evolving company and get dynamic opportunities that go beyond borders.
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
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
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Global Payments, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Global Payments AI Hiring
Global Payments has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Alpharetta, GA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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