American Express
Campus Graduate Masters Summer Internship Program - 2027 AI Engineer, Enterprise Technology Services- Phoenix, AZ
Phoenix, AZ · Hybrid · Internship · Posted Nov 2, 2026
- Education
- Currently enrolled full-time in a Master's degree program; expected graduation between December 2027 and June 2028.
- Visa sponsorship
- Employment eligibility to work in the United States is required; the company will not pursue visa sponsorship for these positions.
About the job
Contribute to building, testing, integrating, and documenting AI-enabled software and services within American Express’s enterprise technology teams.
What you'll do
- Support development and integration of AI/ML models, LLM integrations, and intelligent services; assist with data preparation and model evaluation.
- Test, debug, and improve AI solutions, documenting technical decisions and supporting reliability, security, governance, and compliance.
- Collaborate with Agile engineering, product, data, risk, security, and business teams on AI-driven solutions.
What they ask for
- Currently enrolled full-time in a Master's program, with expected graduation between December 2027 and June 2028.
- Knowledge of Python and foundational data processing technologies; understanding of computer science fundamentals and machine learning concepts.
- 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; strong communication and collaboration skills.
Nice to have
- Experience with Python, R, Java, JavaScript, or similar technologies through coursework, research, projects, or other activities.
- Experience or familiarity with AI-powered applications, NLP, prompt engineering, APIs, data pipelines, cloud environments, or collaborative software engineering workflows.
Pay
$24.05 - $63 hourly (base salary) + sign-on bonus
Tools
Python, R, Java, JavaScript, LLM APIs, SQL, BERT, transformers
AI focus
Supports machine learning, generative AI, LLM integrations, retrieval patterns, AI agents and agentic workflows, model evaluation, and responsible enterprise AI delivery.
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