Agentic AI Engineer - Global Infrastructure

The Enterprise Technology Services organization partners with every part of the American Express business to power the company’s growth and innovation with trust and efficiency, and drive competitive differentiation with speed. We support the delivery and operations of technology, digital, and data capabilities, platforms, and services globally.

Specifically, our team is responsible for the company’s technology engineering, architecture, and infrastructure, providing 24x7 support to ensure an uninterrupted, high-quality experience for customers and colleagues. We also provide product management for core enterprise platforms, and lead technology risk and information security, enterprise data governance and platforms, digital product and design, and enterprise AI platforms on behalf of the company.

This AI Engineer II will build and optimize trained AI/ML models, enhance data pipelines for scalability, support model integration with production systems, and ensure adherence to enterprise AI standards while collaborating cross-functionally to refine AI strategies.

Qualifications

• Education and Knowledge: Bachelor's degree in Computer Science, Machine Learning, Data Science, and/or comparable experience; advanced degree preferred
• Knowledge of Python and data processing technologies
• Knowledge of natural language processing techniques like fuzzy wuzzy and models such as BERT, Transformer models, and their real-world applications
• Working knowledge of machine learning techniques and model evaluation approaches
• Understanding of modern AI architectures including LLM integration patterns, retrieval-augmented generation (RAG), and basic orchestration concepts
• Work Experience: Experience in software development with a focus on machine learning, generative AI, Big Data, and prompt engineering
• Experience in working with large language models (LLMs), especially GPT-3.5/4 or similar AI models
• Experience in languages such as Python or R
• Experience in deploying AI models and pipelines using machine leanring operations tools
• Experience in machine learning algorithms and experience applying them to real-world problems
• Experience in continuous integration/continuous deployment pipelines and containerized deployments
• Experience in building and maintaining data pipelines and extract, transform, and load processes
• Experience developing and deploying machine learning or LLM-enabled applications in production or near-production environments
• Practical experience with distributed systems concepts, APIs, and cloud-native infrastructure supporting AI workloads
• Moderate expertise in a specific technical domain
• 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

• Develops and optimizes AI/ML models, ensuring seamless integration with production environments
• Builds and enhances data pipelines to support scalable and efficient AI processing
• Participates in designing and reviewing AI architectures, ensuring alignment with enterprise standards
• Implements model monitoring and evaluation techniques to improve AI system performance over time
• Ensures AI solutions adhere to governance, security, and compliance requirements
• Provides support and troubleshooting for AI models and data processing pipelines in production
• Collaborates effectively with teams in product and the business to align technology initiatives with business objectives
• Performs standard tasks within established procedures and guidelines; works within established guidelines with some degree of independence for routine tasks
• Develops and integrates components of production AI systems, including ML models or LLM-based services, with moderate independence
• Contributes to AI capabilities such as model training pipelines, inference services, and retrieval workflows, improving performance and reliability
• Applies enterprise standards for observability, safety, and compliance in implemented AI components