AI & Data Platform Engineer

Global Payments Inc.•Alpharetta, GA

About The Position

Ready to take your career global? Make your mark at one of the biggest names in payments. We’re looking for an AI Platform Engineer to join our ever evolving Data & AI team and help shape the future of global commerce. You’ll build and evolve the secure, scalable AI platform capabilities that enable data scientists, machine learning engineers and application teams to move AI solutions from experimentation to production with confidence. From model deployment and observability to automation, generative AI workflows and cost optimization, your work will directly influence how quickly and reliably Global Payments can turn AI innovation into real business and client impact. You’ll have the opportunity to shape how AI is engineered and scaled across a global organization while helping build the technology foundation for the future of payments.

Requirements

4 to 8 years of hands-on experience building, automating, deploying or operating AI and machine learning platforms, with demonstrated success supporting production environments.
Strong engineering capabilities with Python, Structured Query Language (SQL), cloud services, Docker, Kubernetes, CI/CD and infrastructure as code, using technologies such as Terraform or equivalent solutions.
Proven experience with machine learning operations (MLOps), large language model operations (LLMOps), model lifecycle tooling such as MLflow, model serving, APIs, monitoring, logging and production troubleshooting.
Experience designing and operating data pipelines, distributed processing and scalable cloud-based architectures, with the judgment to solve complex technical challenges and improve platform reliability.
A relevant degree in quantitative analytics, statistics, mathematics, data science or a related discipline, combined with a collaborative and innovative mindset and the ability to influence technical decisions across teams.

Nice To Haves

Experience with product analytics and using data and insights to influence product or platform decisions.
A master’s degree in quantitative analytics, statistics, mathematics, data science or a related discipline.
Experience with analytics, distributed data or visualization technologies such as Spark, Hadoop, Power BI, Tableau or similar platforms.

Responsibilities

Build and evolve scalable AI platform capabilities that enable teams to train, deploy, monitor and scale artificial intelligence and machine learning solutions reliably, securely and efficiently.
Accelerate AI delivery from experimentation to production by creating automated model deployment, data and feature pipelines, evaluation workflows and continuous integration and continuous delivery (CI/CD) capabilities that improve speed, consistency and quality.
Strengthen the reliability of production AI through robust observability, model monitoring, logging and evaluation capabilities that help teams identify issues faster, maintain performance and confidently operate AI at scale.
Shape the foundation for generative AI innovation by advancing model serving, application programming interfaces (APIs), prompt and Retrieval-Augmented Generation (RAG) workflows and vector infrastructure that enable teams to build new AI-powered experiences.
Optimize the performance and economics of AI at scale by influencing platform architecture, cloud infrastructure and engineering standards that improve efficiency, security, resilience and cost effectiveness.