Infrastructure Engineer III

As part of our diverse tech team, you can architect, code, and ship software that makes us an essential part of our customers’ digital lives. Here, you can work alongside talented engineers in an open, supportive, and inclusive environment where your voice is valued, and you make your own decisions on what tech to use to solve challenging problems. American Express offers a range of opportunities to work with the latest technologies and encourages you to support the broader engineering community through open source. And because we understand the importance of keeping your skills fresh and relevant, we give you dedicated time to invest in your professional development. Find your place in technology on #TeamAmex.

Within Global Infrastructure & Operations (GIO), Platform Services builds and operates the cloud platforms and infrastructure capabilities that enable engineering teams across American Express.

We are looking for an **AI Engineer III – Cloud Infrastructure** to help engineering teams accelerate how they adopt, deploy, and operate applications in the cloud.

This is a hands-on, forward-deployed engineering role at the intersection of **cloud infrastructure, solution architecture, and AI-assisted engineering**. You will embed with engineering teams, deep-dive into their applications and infrastructure, identify barriers to cloud adoption, and build practical solutions that help workloads become cloud-ready.

You will work across **AWS, Google Cloud Platform (GCP), Microsoft Azure, Kubernetes, and enterprise platform services**, using AI and automation to accelerate infrastructure engineering, solution design, migration, deployment, troubleshooting, and operations.

This is not a research or model-development role. The focus is applying AI to solve real cloud infrastructure problems and make it easier and faster for engineering teams to consume enterprise cloud platforms.

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Embed with application, cloud, and platform engineering teams to understand workloads, architecture, infrastructure requirements, and barriers to cloud adoption.

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Perform hands-on technical deep dives across applications and infrastructure, including compute, Kubernetes, networking, storage, identity, security, observability, CI/CD, and cloud services.

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Work as a hands-on solutions architect: assess existing architectures, identify gaps, design target-state solutions, build prototypes, and work alongside engineers through implementation.

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Use AI-assisted and agentic engineering approaches to accelerate cloud architecture, infrastructure configuration, deployment, troubleshooting, and operational readiness.

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Build practical AI-powered tools and automation that help engineers understand environments, generate and validate infrastructure configurations, troubleshoot cloud issues, and navigate enterprise cloud platforms.

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Help application teams become cloud-ready by addressing infrastructure dependencies, deployment patterns, security controls, networking, observability, resiliency, and operational requirements.

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Develop reference implementations and working examples that demonstrate how applications can successfully consume AWS, GCP, Azure, Kubernetes, and enterprise platform capabilities.

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Troubleshoot complex infrastructure problems end-to-end, following issues across applications, Kubernetes, cloud services, networking, IAM, configuration, telemetry, and deployment pipelines.

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Help teams apply cloud-native patterns across compute, containers, serverless, storage, networking, APIs, and event-driven architectures.

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Use AI to simplify infrastructure consumption and create more intelligent developer self-service experiences, reducing the expertise and manual effort required for teams to use cloud platforms.

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Partner with cloud security, network engineering, SRE, operations, FinOps, and platform teams to develop solutions that meet enterprise requirements for security, reliability, scalability, and cost efficiency.

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Turn lessons learned from individual engineering engagements into reusable automation, patterns, tools, and platform capabilities that benefit the broader engineering community.

Technical Environment

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Cloud: AWS, GCP, Azure

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• Platforms: Kubernetes, containers, serverless
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• Infrastructure: Infrastructure as Code, cloud APIs and SDKs
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• Engineering: Python, Go and/or TypeScript
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• Integration: REST, gRPC, Kafka and event-driven architectures
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• DevOps: CI/CD, Git, automated testing
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• Operations: Observability, logging, metrics and tracing
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• AI: LLMs, AI-assisted development, agentic workflows, tool calling and automation

• 4+ years of professional experience in cloud engineering, software engineering, platform engineering, SRE, solutions engineering, or a related field.
• Hands-on experience designing, deploying, or troubleshooting solutions on AWS, GCP, Azure, or comparable public cloud platforms.
• Strong understanding of cloud architecture and infrastructure concepts including compute, containers, networking, storage, identity, security, and observability.
• Experience with Kubernetes, APIs, infrastructure automation, CI/CD, or developer platforms.
• Software engineering experience in at least one language such as Python, Go, or TypeScript.
• Experience using AI-assisted engineering tools or building practical AI-powered automation.
• Ability to quickly understand unfamiliar applications and infrastructure, identify technical constraints, and develop practical solutions.
• Strong hands-on troubleshooting skills and the ability to work across application and infrastructure boundaries.
• Ability to work directly with engineering teams from problem discovery and architecture through implementation and production readiness.
• Strong customer mindset, curiosity, and comfort working through ambiguous technical problems.

Preferred Qualifications

• Experience across multiple public cloud platforms including AWS, GCP, and Azure.
• Experience in solutions engineering, solutions architecture, forward-deployed engineering, cloud enablement, or platform engineering role.
• Experience helping applications migrate to or adopt cloud-native platforms.
• Experience with Infrastructure as Code, Kubernetes, cloud networking, IAM, observability, or SRE practices.
• Experience applying AI or automation to cloud engineering, infrastructure operations, developer productivity, or troubleshooting.
• Experience creating reusable cloud patterns, reference architectures, automation, or developer self-service capabilities.
• Experience in financial services or another regulated environment.

Depending on factors such as business unit requirements, the nature of the position, cost and applicable laws, American Express may provide visa sponsorship for certain positions