Business Unit / Role Specific Info:
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.
Minimum Qualifications:
- Currently enrolled in a Master’s degree program in Computer Science, Machine Learning, Data Science, Computer Engineering, or another technical field.
- Candidates with an expected graduation date in 2028
- 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.
Employment eligibility to work with American Express in the United Kingdom is required as the company will not pursue visa sponsorship for these positions.
AI Engineer Areas and Skills:
AI Engineer Interns may support teams based on business needs, project requirements, and individual strengths. 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
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 computer science concepts such as data structures, algorithms, object-oriented programming, debugging, testing, and problem-solving.
- Foundational knowledge of machine learning concepts such as supervised learning, unsupervised learning, feature engineering, model evaluation, and basic experimentation.