Infrastructure Engineer Lead, Cloud AI

American Electric Power (AEP)

Confirmed live today High trust

Quick summary

Work type
On-site
Location
Gahanna, OH
Salary
$136,539–$177,503 / yr
Posted
2 days ago
Freshness
Confirmed live today
Closes
Oct 3, 2026

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $191k
This role $157k
$123k most similar roles pay here $248k

This role pays less than 69% of similar roles. Most pay $144,968–$236,100 — the shaded band above. At the midpoint, this role pays about $157k versus about $191k for comparable roles.

Based on 240 similar postings.

Employer

About American Electric Power (AEP)

American Electric Power (AEP) is one of the largest electric utilities in the United States, delivering electricity to customers across 11 states through an extensive transmission and distribution network. Industry: Electric Utilities

American Electric Power (AEP) currently has 25 open roles on FindRole.

Listed pay typically runs $110,000–$129,000 across 24 roles with salary data.

Most-posted roles

View all roles at American Electric Power (AEP)

At a glance

TL;DR · Infrastructure Engineer Lead, Cloud AI

Infrastructure Engineer Lead – Cloud AI The Infrastructure Engineer Lead – Cloud AI joins the engineering team to build and operate secure, scalable infrastructure for machine learning workloads across AWS and on-premises environments. This role involves designing landing zones using Terraform, managing GPU compute, and operating services like Amazon Bedrock and SageMaker. The engineer will develop infrastructure-as-code modules, manage Kubernetes environments including EKS and OpenShift, and oversee networking components such as VPCs, Transit Gateways, and firewalls. Key responsibilities include implementing FinOps practices for cost optimization, ensuring security compliance, and managing observability through logging and alerting. The role addresses the technical challenge of providing reliable infrastructure for AI models in both connected and disconnected environments. Required skills include proficiency in Terraform, Python, Ansible, Kubernetes, and core networking fundamentals to support high-throughput storage and secure data access paths for enterprise-scale AI applications.

What you'll do

  • Build and maintain AWS account structures, landing zones, and infrastructure-as-code modules for AI and machine learning workloads.
  • Manage specialized hardware requirements including GPU instances, high-throughput storage, and networking for AI models.
  • Operate and provision AI platform services such as Amazon Bedrock and SageMaker with proper security and logging.
  • Design and maintain on-premises, edge, and disconnected infrastructure for localized AI compute and model distribution.
  • Implement FinOps practices to manage, forecast, and optimize costs for GPU compute, inference, and token consumption.
  • Deploy and secure Kubernetes environments including EKS or OpenShift with automated scaling and GPU scheduling.
  • Configure networking components like VPCs, Transit Gateways, and load balancers to ensure secure connectivity across hybrid environments.
  • Develop monitoring dashboards, alerting systems, and incident response protocols for AI infrastructure performance and reliability.

What we're looking for

  • Bachelor's degree in computer science, engineering, or a related technical field is required.
  • 12 years of relevant work experience are required.
  • Demonstrated hands-on experience with AWS services including IAM, VPC networking, compute, storage, and encryption.
  • Strong Kubernetes expertise in cluster design, operations, troubleshooting, and security for EKS, ROSA, or OpenShift.
  • Solid networking fundamentals including routing, DNS, load balancing, firewalls, and hybrid connectivity for disconnected environments.
  • Proficiency with Terraform is required; Ansible, Python, or PowerShell are preferred.
  • Experience implementing monitoring, cost management (FinOps), and security for cloud and on-premises environments.
  • AWS Solutions Architect Associate/Professional, AWS Certified Machine Learning, or Certified Kubernetes Administrator (CKA) certifications are desired.

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