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.
At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. From delivering differentiated products to providing world-class customer service, we operate with a strong risk mindset, ensuring we continue to uphold our brand promise of trust, security, and service.
As part of Team Amex, you'll experience our powerful backing with comprehensive support for your holistic well-being and many opportunities to learn new skills, develop as a leader, and grow your career. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express.
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.
- Mentors junior Infrastructure Engineers and cross-functional team of colleagues, fostering a culture of excellence and innovation
- Leads projects collaborating with multiple cross-functional teams within budget and schedule, ensuring timely issue resolution and maintaining high standards of design, implementation, testing, and compliance
- Guides stakeholders on aligning processes with industry best practices and business objectives
- Provides guidance and support to junior engineers, fostering professional growth and development within the team
- Collaborates with application teams to integrate business needs into comprehensive infrastructure solutions, aligning with business objectives and platform roadmap
- Leads infrastructure design and selection, ensuring solutions meet customer needs and strategic goals
- Diagnoses and resolves complex infrastructure issues, providing support during outages to ensure minimal disruption
- Monitors and addresses capacity and availability issues, conducts performance and security assessments, and supports certification and accreditation processes
- Leads the efforts towards defining and maintaining processes, documentation, and engineering diagrams for technical and operational support
- Embeds AI-assisted observability, automation, and documentation capabilities into team workflows to improve deployment efficiency, issue detection, infrastructure analysis, troubleshooting, production support, system readiness, and operational support for specialized compute environments supporting AI-enabled workloads.
- Embeds ML-powered AIOps and custom anomaly detection into team workflows, using metrics, logs, and traces to detect anomalies, predict failures, and correlate incidents across distributed systems.
- Embeds AI-assisted infrastructure-as-code development and review practices using tools such as GitHub Copilot, Claude Code, Cursor, and similar capabilities into engineering workflows, improving consistency, deployment quality, and operational efficiency.
- Supports infrastructure environments hosting AI-enabled workloads, including cloud-based LLM platforms such as AWS Bedrock, Azure AI Foundry/Azure OpenAI, and Google Vertex AI, while mentoring engineers on responsible use of AI-enabled infrastructure tools, prompt engineering, runbook generation, incident troubleshooting, postmortem summarization, operational analysis, validation, oversight, and operational readiness of GPU- and TPU-enabled infrastructure environments.
- Designs and operates GPU- and accelerator-enabled compute platforms, including cluster scheduling, workload placement, capacity management, quota and reservation controls, and utilization optimization.
- Builds reusable platform capabilities for model training, fine-tuning, inference, and agentic workloads using Kubernetes, containers, infrastructure as code, and automated delivery pipelines.
- Engineers high-performance storage, networking, and data-movement patterns required for distributed AI workloads, with measurable service-level objectives for availability, latency, throughput, and recovery.
- Implements end-to-end observability and FinOps controls for AI infrastructure, including workload telemetry, accelerator utilization, cost attribution, anomaly detection, performance tuning, and capacity forecasting.
- Partners with AI engineers, security, risk, and platform teams to establish secure multi-tenant isolation, identity and access controls, secrets management, software supply-chain controls, and operational readiness.
- Mentors junior Infrastructure Engineers and cross-functional team of colleagues, fostering a culture of excellence and innovation
- Leads projects collaborating with multiple cross-functional teams within budget and schedule, ensuring timely issue resolution and maintaining high standards of design, implementation, testing, and compliance
- Guides stakeholders on aligning processes with industry best practices and business objectives
- Provides guidance and support to junior engineers, fostering professional growth and development within the team
- Collaborates with application teams to integrate business needs into comprehensive infrastructure solutions, aligning with business objectives and platform roadmap
- Leads infrastructure design and selection, ensuring solutions meet customer needs and strategic goals
- Diagnoses and resolves complex infrastructure issues, providing support during outages to ensure minimal disruption
- Monitors and addresses capacity and availability issues, conducts performance and security assessments, and supports certification and accreditation processes
- Leads the efforts towards defining and maintaining processes, documentation, and engineering diagrams for technical and operational support
- Embeds AI-assisted observability, automation, and documentation capabilities into team workflows to improve deployment efficiency, issue detection, infrastructure analysis, troubleshooting, production support, system readiness, and operational support for specialized compute environments supporting AI-enabled workloads.
- Embeds ML-powered AIOps and custom anomaly detection into team workflows, using metrics, logs, and traces to detect anomalies, predict failures, and correlate incidents across distributed systems.
- Embeds AI-assisted infrastructure-as-code development and review practices using tools such as GitHub Copilot, Claude Code, Cursor, and similar capabilities into engineering workflows, improving consistency, deployment quality, and operational efficiency.
- Supports infrastructure environments hosting AI-enabled workloads, including cloud-based LLM platforms such as AWS Bedrock, Azure AI Foundry/Azure OpenAI, and Google Vertex AI, while mentoring engineers on responsible use of AI-enabled infrastructure tools, prompt engineering, runbook generation, incident troubleshooting, postmortem summarization, operational analysis, validation, oversight, and operational readiness of GPU- and TPU-enabled infrastructure environments.
- Designs and operates GPU- and accelerator-enabled compute platforms, including cluster scheduling, workload placement, capacity management, quota and reservation controls, and utilization optimization.
- Builds reusable platform capabilities for model training, fine-tuning, inference, and agentic workloads using Kubernetes, containers, infrastructure as code, and automated delivery pipelines.
- Engineers high-performance storage, networking, and data-movement patterns required for distributed AI workloads, with measurable service-level objectives for availability, latency, throughput, and recovery.
- Implements end-to-end observability and FinOps controls for AI infrastructure, including workload telemetry, accelerator utilization, cost attribution, anomaly detection, performance tuning, and capacity forecasting.
- Partners with AI engineers, security, risk, and platform teams to establish secure multi-tenant isolation, identity and access controls, secrets management, software supply-chain controls, and operational readiness.