Senior Agentic AI Infrastructure Engineer I - Core Platforms

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.

The AI Infrastructure Engineer collaborates with application and platform teams to design, implement, and optimize secure, scalable infrastructure solutions for AI-enabled workloads. This role resolves complex technical issues, ensures strong system performance, reliability, and security, and leads infrastructure initiatives that align with business objectives and platform strategy.

Work Experience

- Designing infrastructure for AI agents, including agent runtimes, orchestration frameworks, tool integrations, asynchronous execution, and stateful workflows. Examples include LangGraph, Semantic Kernel, and comparable frameworks.
- Experience applying Agentic AI tools to support infrastructure engineering, platform support, or cloud operations work, including reviewing AI-generated operational insights.
- Experience validating AI-generated operational outputs and incorporating AI-enabled practices into infrastructure workflows, including AI-assisted operational recommendations requiring human review and approval.
- Experience with Kubernetes-based AI platforms, containers, workload schedulers, and GPU or accelerator orchestration in cloud or hybrid environments.
- Experience operating model-serving or inference platforms and optimizing throughput, latency, availability, and cost for production AI workloads.
- Experience with distributed training or inference infrastructure, high-performance networking and storage, accelerator telemetry, and capacity planning.
- Experience implementing security, reliability engineering, observability, and FinOps practices for multi-tenant AI infrastructure.
- Experience in different programming languages, DevOps tools such as Ansible, Puppet, Terraform, Python, Shell scripting and PowerShell, and their applications in infrastructure.
- Experience in infrastructure components such as servers, storage, networking, and virtualization technologies.
- Experience in network protocols and their job in infrastructure management.
- Experience in networking fundamentals.
- Experience in databases.
- Experience in tools used in planning, analyzing, crafting, building, testing, configuring, and maintaining applications.
- Experience in cloud platforms (e.g., AWS, Azure, Google Cloud).
- Experience in monitoring technologies such as Prometheus, ElasticSearch and Grafana.

Education and Knowledge

- 5+ Years of experience in infrastructure engineering, cloud engineering, DevOps, or a related technical field, including at least 2 years of hands-on experience supporting AI-enabled infrastructure, AI automation, or agentic AI technologies.
- Bachelor's degree in Computer Science, Information Technology, Engineering, and/or comparable experience; advanced degree preferred
- Knowledge of application infrastructure and standards
- Knowledge of storage management (e.g., SAN, NAS) design and support
- Knowledge of middleware technologies
- Knowledge of software defined networking, automation, and orchestration and their applications
- Knowledge of platform delivery methodology (Agile, Scrum, Agile XP, Best in Class Development, Waterfall, etc.)
- Knowledge of scripting and automation skills (e.g., PowerShell, Bash, Python)
- Knowledge of DevOps practices and tools (e.g., continuous integration/continuous deployment pipelines, Docker, Kubernetes)
- Knowledge of cloud technologies
- Knowledge of Windows and Linux administration
- Working knowledge of AI-assisted infrastructure automation, observability, and deployment support capabilities, and infrastructure services commonly used to support AI-enabled workloads
- Working Knowledge of AI use cases in configuration management, incident analysis, infrastructure operations, and AI-assisted operational workflows that incorporate human oversight and approval controls.

Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.