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# Sr AI Engineer I

- **Company:** American Express Company
- **Location:** Mesa, AZ, United States
- **Experience:** Expert
- **Salary:** $123,000.0 - $215,250.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Encodings, Databases, Information Engineering, Python (Programming Language), Operational Data Store, Software Engineering, Unstructured Data, Chatbots, Large Language Models, Reliability of Systems, Generative AI, Containerization, Kubernetes, HuggingFace, Data Pipelines, Docker, Microservices
- **Published:** September 23, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3398917474&tx=DT10798UYV&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* 2-5 years of software engineering or data engineering experience
 * Hands-on experience building solutions with demonstrated results using LLM-based applications (RAG, chatbots, or similar)
 * Proficiency in Python (preferred) or Go
 * Understanding of data pipelines and working with structured and unstructured data
 * Familiarity with modern AI/ML tooling (e.g., LangChain, Hugging Face, or similar ecosystems)
 * Experience deploying applications in cloud or containerized environments (Docker, Kubernetes, etc.)

Preferred Qualifications

 * Experience working with vector databases, traditional databases and warehouses, and embedding-based retrieval
 * Exposure to observability and/or operational data (logs, metrics, tracing)
 * Experience with ITIL data, particularly incident, problem, and change
 * Familiarity with APIs, microservices, or full-stack development
 * Interest in applying AI to system reliability and operational effectiveness.
 * Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.

## Description

* Build and iterate on Generative AI applications, including retrieval-augmented generation (RAG) systems and lightweight agent workflows
 * Develop data pipelines that bring together logs, metrics, and operational data to support AI-driven use cases
 * Prototype quickly and help transition successful ideas into production-ready tools
 * Apply LLMs, embeddings, and vector databases to solve problems in incident management and system operations
 * Collaborate with engineers and product partners to identify high-impact opportunities for AI in production environments
 * Contribute to deployment, monitoring, and ongoing improvement of AI-powered systems

What You'll Learn / Grow Into

 * Designing and scaling AI systems in real-world production environments
 * Applying GenAI patterns (RAG, agents) to complex and diverse enterprise data
 * Building reliable, observable systems-not just prototypes, with measurable effectiveness and outcomes
 * Working across teams to turn ideas into adopted mainstream solutions


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