Senior Agentic AI Infrastructure Engineer - Core Platforms

Position: Senior Agentic AI Infrastructure Engineer I - Core Platforms
Job d:

Location:

New York, NY, United States;
Sunrise, FL, United States;
Phoenix, AZ, United States Salary: $123000 - $215250 annually + bonus + benefits

Job Function:
Engineering & Architecture

Schedule:

Full time

Shift: Day Workplace:
Hybrid Career Area:
Technology Company:
American Express 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. Responsibilities 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 Git Hub 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…