Senior Agentic AI Infrastructure Engineer I - Core Platforms

Aimlroles; New York (NY); On-site; USD 150,000 - 210,000; Full time

Job summary

American Express is seeking an AI Infrastructure Engineer to design, implement, and optimize scalable, secure AI-enabled infrastructure. You will collaborate with application and platform teams to ensure high performance, reliability, and governance across cloud environments.

You will mentor engineers, lead cross-functional projects, and embed AI-assisted observability and automation throughout the lifecycle, from design to production support.

Qualifications

• 5+ years in infrastructure engineering, cloud engineering, DevOps or related fields.
• Hands-on AI-enabled infrastructure, AI automation, or agentic AI tech experience.
• Bachelor’s in CS/IT/Engineering or equivalent experience.
• Knowledge of storage, middleware, networking, automation, and orchestration.
• Familiarity with cloud platforms (AWS/Azure/Google Cloud) and AI instrumentation.

Responsibilities

• Design, implement, and optimize AI-enabled infrastructure for scalable workloads.
• Lead multi-team projects within budget and schedule while ensuring security and reliability.
• Embed AI-assisted observability, automation, and governance into pipelines.
• Mentor junior engineers and partner with security, risk, and platform teams.
• Ensure secure multi-tenant isolation, identity controls, and software supply-chain safeguards.

Skills

Infrastructure design

AI workloads

Kubernetes

DevOps practices

Python

PowerShell

Shell scripting

Terraform

Ansible

Puppet

GitHub Copilot / AI-assisted coding

Education

Bachelor's degree in Computer Science, Information Technology, Engineering

Advanced degree preferred

Tools

Kubernetes

Terraform

Ansible

Puppet

Docker

AWS

Azure

Google Cloud

Prometheus

Grafana

Elasticsearch

Job description

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.

• 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.

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.