Campus Undergraduate Summer Internship Program

About the Role

The Enterprise Technology Services organization partners with every part of American Express to drive growth and innovation, supporting technology, digital, and data capabilities globally. Our team is responsible for technology engineering, architecture, and infrastructure, providing 24x7 support. We also manage enterprise AI platforms, technology risk, information security, and data governance.

As an AI Engineer Intern, you will join a 10-week Summer Internship Program. You will contribute to real-world technology projects focused on exploring, building, testing, and scaling AI-enabled solutions. You will build software, collaborate with Agile teams, and learn how products are designed, developed, tested, and delivered in a global enterprise environment. This role may involve work across machine learning, generative AI, intelligent automation, data pipelines, retrieval patterns, model evaluation, AI agents, agentic workflows, or AI-enabled software features. You will 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.

Key Responsibilities

• Support the development and integration of AI/ML models, LLM integrations, or intelligent services into controlled or production-like systems.
• Assist with data collection, preprocessing, transformation, and management for model training, testing, validation, and evaluation.
• Contribute to testing, debugging, and improving AI-enabled solutions to enhance 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.
• Document model parameters, prompts, evaluation assumptions, data pipelines, system integrations, and technical decisions for reproducibility.
• Participate in Agile development practices, including sprint planning, stand-ups, demos, retrospectives, and code reviews.
• 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 work, and share outcomes.
• How to grow your career through mentorship, feedback, peer learning, technical curriculum, and Early Careers programming.
• Foundational knowledge of computer science concepts (data structures, algorithms, object-oriented programming, debugging, testing, problem-solving).
• Foundational knowledge of machine learning concepts (supervised learning, unsupervised learning, feature engineering, model evaluation, experimentation).

Qualifications

Currently enrolled in a full-time bachelor’s degree program with an expected graduation date between December 2027 and June 2028.

Minimum Qualifications

• Knowledge of Python and foundational data processing technologies.
• Foundational understanding of computer science concepts, including data structures, algorithms, debugging, testing, and problem-solving.
• Understanding of machine learning concepts such as model training, evaluation, feature engineering, and experimentation.
• Experience using modern AI systems such as LLM APIs, prompt-based interactions, retrieval patterns, or generative AI applications.
• Awareness of responsible AI, security, governance, compliance, and reliability considerations.
• Strong communication, collaboration, documentation, and learning agility with the ability to work effectively in a team environment.

Preferred Qualifications

• Demonstrated experience through academic coursework, research, projects, open source contributions, internships, or extracurricular activities using Python, R, Java, JavaScript, or similar technologies.
• Interest in machine learning, generative AI, natural language processing, intelligent automation, data engineering, agentic AI, or AI-enabled software development.
• Experience building AI-powered applications, copilots, intelligent assistants, agentic workflows, research prototypes, or hackathon solutions using AI/ML technologies.
• Familiarity with NLP techniques and model concepts such as fuzzy matching, embeddings, BERT, transformers, or other modern language models.
• Exposure and experience with prompt engineering, prompt evaluation, tools, function calling, or agent workflow concepts.
• Experience or coursework involving ML algorithms and applying them to practical or real-world problems.
• Familiarity with APIs, data pipelines, ETL processes, cloud environments, or containerized development.
• Awareness of CI/CD, version control, testing, code reviews, and collaborative software engineering workflows.
• Curiosity for AI-powered developer tools, responsible AI practices, governance, security, and enterprise-scale delivery.

AI Engineer Areas and Skills

Experience in one or more of the following areas is beneficial:

• AI/Machine Learning Engineering: Python, R, Java, machine learning fundamentals, model training, model evaluation, feature engineering, NLP, embeddings, transformer models, LLM APIs, prompt engineering, retrieval patterns, AI agents, model documentation, responsible AI concepts.
• Data Engineering for AI: Data collection, preprocessing, data quality, ETL, data pipelines, SQL, big data concepts, data validation, feature pipelines, and reproducible data workflows.
• AI-Enabled Software Engineering: APIs, microservices, inference endpoints, application integration, cloud-native development, agile delivery, testing, CI/CD, containerization, observability, and production-like deployment practices.
• Generative AI/LLM Applications: Prompt-based interactions, LLM integrations, retrieval-augmented generation concepts, evaluation of AI outputs, grounding patterns, guardrails, AI agents, agent orchestration, and human-in-the-loop review.
• Enterprise AI Readiness: Security, compliance, model governance, documentation, risk awareness, system reliability, issue escalation, and responsible AI practices.
• Cybersecurity & AI Security: Secure software development practices, application security fundamentals, identity and access management, data protection, encryption concepts, secure API design, vulnerability awareness, threat modeling fundamentals, secure use of AI/LLM technologies, AI security risks (prompt injection, data leakage, model abuse), governance controls, compliance awareness, and responsible handling of sensitive information.

American Express will not pursue visa sponsorship for these positions.