Campus Undergraduate Summer Internship Program - 2027 AI Engineer, Enterprise Technology Services- Sunrise, FL

Business Unit/Role Specific Information

The Enterprise Technology Services organization partners with every part of the American Express business to power the company’s growth and innovation with trust and efficiency, and drive competitive differentiation with speed. We support the delivery and operations of technology, digital, and data capabilities, platforms, and services globally. Specifically, our team is responsible for the company’s technology engineering, architecture, and infrastructure, providing 24x7 support to ensure an uninterrupted, high-quality experience for customers and colleagues. We also provide product management for core enterprise platforms, and lead technology risk and information security, enterprise data governance and platforms, digital product and design, and enterprise AI platforms on behalf of the company. 

At American Express, we empower future technologists to learn, innovate, and make an impact from day one. As an AI Engineer Intern in Enterprise Technology Services, you’ll join a 10-week Summer Internship Program and contribute to real-world technology projects that help teams explore, build, test, and responsibly scale AI-enabled solutions. You’ll build software, collaborate with Agile teams, and learn how products are designed, developed, tested, and delivered in a global enterprise environment. 

In this role, you may support work across machine learning, generative AI, intelligent automation, data pipelines, retrieval patterns, model evaluation, AI agents, agentic workflows, or AI-enabled software features. You’ll work with engineers, product partners, data practitioners, security partners, and business stakeholders to learn how 
enterprise AI solutions are designed and delivered responsibly, reliably, and securely. 

About the Team 

Enterprise Technology Services teams build and operate technology that helps American Express deliver trusted, secure, and customer-first products and services.  Interns may be aligned to scrum teams across backend engineering, frontend engineering, cloud engineering, mobile, AI/machine learning, data-oriented engineering, 
or full-stack product development.  

As an AI Engineer Intern, you’ll contribute at an early-career level while learning how intelligent systems are built, validated, integrated, monitored, and governed in an enterprise environment. 
 

What type of work can you expect? How will you make an impact in this role? 

• Support the development and integration of AI/ML models, LLM integrations, or intelligent services into controlled or production-like systems under guidance. 
• Assist with data collection, preprocessing, transformation, and management to enable model training, testing, validation, and evaluation. 
• Contribute to testing, debugging, and improving AI-enabled solutions to strengthen performance, reliability, explainability, and maintainability. 
• Support AI capabilities such as basic model training workflows, inference endpoints, prompt based interactions, evaluation routines, data retrieval pipelines, AI agents, or agentic workflows. 
• Collaborate with engineering, product, data, risk, security, and business partners to implement AI driven solutions aligned to business requirements. 
• Document model parameters, prompts, evaluation assumptions, data pipelines, system integrations, and technical decisions to support reproducibility. 
• Participate in Agile development practices, including sprint planning, stand ups, demos, retrospectives, code reviews, and team ceremonies. 
• Assist in ensuring AI systems and AI enabled features align with enterprise expectations for reliability, safety, governance, security, and compliance. 
• Build foundational confidence working across AI-adjacent technology areas such as APIs, cloud environments, data platforms, CI/CD, containers, model deployment patterns, and monitoring. 
 

What You’ll Learn 

• How AI-enabled software is designed, built, tested, and delivered in an enterprise technology environment. 
• How machine learning, generative AI, LLM APIs, prompt-based workflows, retrieval patterns, AI agents, agentic workflows, and model evaluation can be applied to business problems. 
• How Product, Engineering, Data, Security, Risk, and business partners collaborate from idea to implementation. 
• How to balance AI innovation with quality, resilience, usability, privacy, security, compliance, and responsible AI expectations. 
• How to communicate technical progress, ask effective questions, document your work, and share outcomes with both technical and non-technical audiences. 
• How to grow your career through mentorship, feedback, peer learning, technical curriculum, and Early Careers programming. 
• Foundational knowledge of comput