Senior Agentic AI Infrastructure Engineer I - Core Platforms

American Express•New York, NY

About The Position

The AI Infrastructure Engineer collaborates with application and platform teams to design, implement, and optimize secure, scalable infrastructure solutions for AI-enabled workloads. The role resolves complex technical issues, ensures strong system performance, reliability, and security, and leads infrastructure initiatives aligned with business objectives and platform strategy. Within American Express Enterprise Technology Services, the team supports technology engineering, architecture, infrastructure, and enterprise AI platforms globally, providing 24x7 support for customers and colleagues. The engineer mentors junior colleagues, leads cross-functional infrastructure projects, advises stakeholders, and develops infrastructure solutions aligned with customer needs, strategic goals, and the platform roadmap. The role supports GPU- and accelerator-enabled compute platforms; reusable capabilities for model training, fine-tuning, inference, and agentic workloads; and high-performance storage, networking, and data movement for distributed AI workloads. It also establishes observability, FinOps, security, reliability, and operational-readiness practices for multi-tenant AI infrastructure. The role incorporates AI-assisted observability, automation, documentation, infrastructure-as-code development, and review into engineering workflows. It applies ML-powered AIOps and anomaly detection to metrics, logs, and traces, and supports cloud AI platforms including AWS Bedrock, Azure AI Foundry/Azure OpenAI, and Google Vertex AI. AI-generated operational outputs and recommendations require validation, human review, and approval. Employment eligibility to work in the United States is required; American Express will not pursue visa sponsorship for these positions.

Requirements

At least 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.
Experience designing infrastructure for AI agents, including agent runtimes, orchestration frameworks, tool integrations, asynchronous execution, and stateful workflows; examples include LangGraph, Semantic Kernel, or comparable frameworks.
Experience applying Agentic AI tools to infrastructure engineering, platform support, or cloud operations, including reviewing AI-generated operational insights.
Experience validating AI-generated operational outputs and incorporating AI-enabled practices into infrastructure workflows, including 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 production AI workload throughput, latency, availability, and cost.
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 with programming languages and DevOps tools such as Ansible, Puppet, Terraform, Python, shell scripting, and PowerShell, and their infrastructure applications.
Experience with infrastructure components such as servers, storage, networking, and virtualization technologies.
Experience with network protocols and their role in infrastructure management, as well as networking fundamentals.
Experience with databases.
Experience with tools used to plan, analyze, craft, build, test, configure, and maintain applications.
Experience with cloud platforms such as AWS, Azure, or Google Cloud.
Experience with monitoring technologies such as Prometheus, Elasticsearch, and Grafana.
Bachelor’s degree in Computer Science, Information Technology, Engineering, or comparable experience.
Knowledge of application infrastructure and standards.
Knowledge of storage management design and support, such as SAN and NAS.
Knowledge of middleware technologies.
Knowledge of software-defined networking, automation, and orchestration and their applications.
Knowledge of platform delivery methodologies, including Agile, Scrum, Agile XP, Best in Class Development, and Waterfall.
Knowledge of scripting and automation, such as PowerShell, Bash, and Python.
Knowledge of DevOps practices and tools, including continuous integration/continuous deployment pipelines, Docker, and Kubernetes.
Knowledge of cloud technologies and Windows and Linux administration.
Working knowledge of AI-assisted infrastructure automation, observability, deployment support, and infrastructure services commonly used for AI-enabled workloads.
Working knowledge of AI use cases in configuration management, incident analysis, infrastructure operations, and AI-assisted operational workflows with human oversight and approval controls.
Eligibility to work in the United States; American Express will not pursue visa sponsorship for these positions.

Nice To Haves

An advanced degree is preferred.

Responsibilities

Mentor junior Infrastructure Engineers and cross-functional colleagues, supporting professional development and fostering excellence and innovation.
Lead infrastructure projects with multiple cross-functional teams, managing budget and schedule and ensuring timely issue resolution and high standards for design, implementation, testing, and compliance.
Guide stakeholders in aligning processes with industry best practices and business objectives.
Collaborate with application teams to incorporate business needs into infrastructure solutions aligned with business objectives and the platform roadmap.
Lead infrastructure design and selection to meet customer needs and strategic goals.
Diagnose and resolve complex infrastructure issues and provide outage support to minimize disruption.
Monitor and address capacity and availability issues; conduct performance and security assessments; and support certification and accreditation processes.
Define and maintain processes, documentation, and engineering diagrams for technical and operational support.
Embed AI-assisted observability, automation, and documentation into workflows to improve deployment efficiency, issue detection, infrastructure analysis, troubleshooting, production support, system readiness, and operational support for AI-enabled compute environments.
Use ML-powered AIOps and custom anomaly detection with metrics, logs, and traces to detect anomalies, predict failures, and correlate incidents across distributed systems.
Incorporate AI-assisted infrastructure-as-code development and review tools, such as GitHub Copilot, Claude Code, and Cursor, into engineering workflows.
Support infrastructure for AI-enabled workloads on cloud LLM platforms, including AWS Bedrock, Azure AI Foundry/Azure OpenAI, and Google Vertex AI; mentor engineers in responsible use of AI tools and validate AI-enabled operational outputs with appropriate oversight.
Design and operate GPU- and accelerator-enabled compute platforms, including cluster scheduling, workload placement, capacity management, quota and reservation controls, and utilization optimization.
Build reusable platform capabilities for model training, fine-tuning, inference, and agentic workloads using Kubernetes, containers, infrastructure as code, and automated delivery pipelines.
Engineer high-performance storage, networking, and data-movement patterns for distributed AI workloads, with measurable availability, latency, throughput, and recovery objectives.
Implement end-to-end observability and FinOps controls for AI infrastructure, including workload telemetry, accelerator utilization, cost attribution, anomaly detection, performance tuning, and capacity forecasting.
Partner with AI engineering, security, risk, and platform teams to establish secure multi-tenant isolation, identity and access controls, secrets management, software supply-chain controls, and operational readiness.