AI Engineer I – Enterprise AI

Job Description

AI Engineer I, Enterprise Technology Services – Campus Graduate Masters Full-Time Engineer | American Express

The Tone:
This is a full-time role at American Express, located in Palo Alto, CA, as part of a graduate program. The Enterprise Technology Services organization powers the company’s growth and innovation, ensuring trust, efficiency, and competitive differentiation through global technology, digital, and data capabilities. As an AI Engineer I, you will contribute to technology work that helps teams explore, build, test, and responsibly scale AI-enabled solutions, making an impact from day one.

The TL;DR
• Role: Early Career
• Type: Full-time
• Location: Hybrid, Palo Alto, CA

• Team: Enterprise Technology Services organization
• Mission: This person solves the problem of responsibly, reliably, and securely delivering enterprise AI solutions.
• Tech Stack: Python, R, Java, JavaScript, Git, LLM APIs

What You’ll Actually Do
• Development: Support the development, testing, and integration of AI / ML models, LLM integrations, intelligent services, or data retrieval pipelines under guidance.
• Data Management: Assist with data collection, preprocessing, transformation, and validation to support model training, testing, evaluation, and implementation.
• Improvement: Contribute to debugging and improving AI enabled solutions to strengthen performance, reliability, explainability, maintainability, and quality.
• Collaboration: Collaborate with engineering, product, data, risk, security, and business partners to implement AI-driven solutions aligned to business requirements.
• Documentation: Document model parameters, prompts, assumptions, data pipelines, integrations, and technical decisions to support reproducibility and knowledge sharing.

The Must-Haves
• Background: Must have earned a Master’s degree in Computer Science, Machine Learning, Data Science, Artificial Intelligence, Computer Engineering, Software Engineering, or another technical field before the full-time start date. This is an early career role.
• Experience: 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.
• Skills: Knowledge of Python and foundational data processing technologies. Strong communication, collaboration, documentation, and learning agility with the ability to work effectively across technical and non-technical teams. Awareness of responsible AI expectations for reliability, safety, governance, security, privacy, compliance, and appropriate escalation.
• Bonus: Master’s degree candidates with an expected graduation date between December 2026 and June 2027. Experience through academic coursework, research, projects, open-source contributions, internships, or hackathons using Python, R, Java, or JavaScript. 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, 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 problems. Familiarity with APIs, data pipelines, ETL processes, cloud environments, containerized development, model deployment patterns, or monitoring. Awareness of CI/CD, version control systems such as Git, testing, code reviews, Agile development, and collaborative software engineering workflows. Curiosity for AI powered developer tools, responsible AI practices, governance, security, model documentation, and enterprise-scale delivery.