At American Express, we empower technologists to learn, innovate, and make an impact from day one. As an AI Engineer I in Enterprise Technology Services, you’ll join a full-time graduate program and contribute to technology work that helps teams explore, build, test, and responsibly scale AI-enabled solutions. 

In this role, you may support work across machine learning, generative AI, intelligent automation, data pipelines, retrieval patterns, model evaluation, LLM integrations, AI agents, agentic workflows, and AI-enabled software features. You’ll collaborate with engineering, product, data, security, risk, and business partners to help deliver enterprise AI solutions responsibly, reliably, and securely. 

Minimum Qualifications 

• Must have earned a Bachelor’s degree in Computer Science, Machine Learning, Data Science, Artificial Intelligence, Computer Engineering, Software Engineering, or another technical field before the end of June 2027.   
• Knowledge of Python and foundational data processing technologies. 
• Foundational understanding of computer science concepts, including data structures, algorithms, object-oriented programming, debugging, testing, and problem solving. 
• Foundational understanding of machine learning concepts such as supervised learning, unsupervised learning, model training, evaluation, feature engineering, and experimentation. 
• Introductory understanding of modern AI systems, including LLM APIs, prompt-based interactions, retrieval patterns, AI powered tools, or generative AI applications. 
• Ability to support AI/ML development, testing, documentation, integration, or data pipeline activities under guidance. 
• Awareness of responsible AI expectations, including reliability, safety, governance, security, privacy, compliance, and appropriate escalation when work is unclear or outside standard guidance. 
• Strong communication, collaboration, documentation, and learning agility with the ability to work effectively across technical and non-technical teams. 

 

Preferred Qualifications 

• Experience through academic coursework, research, projects, open-source contributions, internships, hackathons, 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. 
• Familiarity with NLP techniques and model concepts such as fuzzy matching, embeddings, BERT, transformers, LLMs, or other modern language models. 
• Exposure to LLM APIs or similar AI models, including prompt engineering, prompt evaluation, tools, function calling, retrieval patterns, or agent workflow concepts. 
• Experience or coursework involving machine learning algorithms and applying them to practical or real-world problems. 
• Familiarity with APIs, data pipelines, ETL processes, cloud environments, containerized development, model deployment patterns, or monitoring. 
• Awareness of CI/CD, version control, testing, code reviews, Agile development, and collaborative software engineering workflows. 
• Exposure to version control systems such as Git and collaborative software development workflows. 
• Curiosity for AI powered developer tools, responsible AI practices, governance, security, model documentation, and enterprise-scale delivery. 

 

Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions. 

Responsibilities & What Type of Work to Expect 

• Support the development, testing, and integration of AI / ML models, LLM integrations, intelligent services, or data retrieval pipelines under guidance. 
• Assist with data collection, preprocessing, transformation, and validation to support model training, testing, evaluation, and implementation. 
• Contribute to debugging and improving AI enabled solutions to strengthen performance, reliability, explainability, maintainability, and quality. 
• Support AI capabilities such as model training workflows, inference endpoints, prompt-based interactions, evaluation routines, retrieval patterns, 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, assumptions, data pipelines, integrations, and technical decisions to support reproducibility and knowledge sharing. 
• 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, compliance, and appropriate escalation.