Campus Graduate Masters Full-Time Engineer

About The Team

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 for a high-quality customer and colleague experience. We also manage core enterprise platforms, technology risk, information security, enterprise data governance, digital product and design, and enterprise AI platforms. At American Express, technologists are empowered to learn, innovate, and make an impact from day one.

About the Role

As an AI Engineer I in Enterprise Technology Services, you will join a full-time graduate program and contribute to technology work that helps teams explore, build, test, and responsibly scale AI-enabled solutions. This role may involve working 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 will collaborate with engineering, product, data, security, risk, and business partners to deliver enterprise AI solutions responsibly, reliably, and securely.

Key Responsibilities

• 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 enhance 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 with 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.

Requirements & Tech Stack

• Python
• Foundational data processing technologies
• Foundational understanding of computer science concepts (data structures, algorithms, object-oriented programming, debugging, testing, problem solving)
• Foundational understanding of machine learning concepts (supervised learning, unsupervised learning, model training, evaluation, feature engineering, experimentation)
• Introductory understanding of modern AI systems (LLM APIs, prompt-based interactions, retrieval patterns, AI powered tools, generative AI applications)
• Ability to support AI/ML development, testing, documentation, integration, or data pipeline activities under guidance.
• Awareness of responsible AI expectations (reliability, safety, governance, security, privacy, compliance, and appropriate escalation)
• Strong communication, collaboration, documentation, and learning agility

Qualifications

• Master’s degree in Computer Science, Machine Learning, Data Science, Artificial Intelligence, Computer Engineering, Software Engineering, or another technical field.
• Expected graduation date between December 2026 and June 2027 (Preferred).
• 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 (e.g., fuzzy matching, embeddings, BERT, transformers, LLMs).
• 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.
• Curiosity for AI powered developer tools, responsible AI practices, governance, security, model documentation, and enterprise-scale delivery.