Infrastructure Engineer Lead – Cloud AI

American Electric Power • Gahanna, OH

1d • Onsite

About The Position

This hands-on role builds and operates secure, scalable infrastructure for AI and machine learning workloads across AWS and approved on-premises environments. Responsibilities include cloud foundations, networking, containers, compute, identity, cost management, and observability. The engineer partners with AI, architecture, cybersecurity, network, and application teams to deliver secure, cost-effective, production-ready infrastructure while supporting core cloud engineering services.

Requirements

• Demonstrated hands-on engineering experience in AWS, including IAM, VPC networking, compute, storage, encryption/KMS, and account/organization structure.
• Strong Kubernetes expertise including cluster design, operations, troubleshooting and security in EKS, ROSA or OpenShift.
• Solid networking fundamentals, including routing, DNS, load balancing, traffic management, firewalls, hybrid connectivity, and network design for disconnected or intermittently connected environments.
• Experience with API gateways, integration patterns, and exposing and securing services across environments.
• Proficiency with infrastructure-as-code and automation (Terraform required; Ansible, Python or PowerShell preferred) and CI/CD pipelines.
• Working knowledge of cloud security principles, identity and access management, and compliance requirements.
• Experience implementing monitoring and cost management for cloud environments, with the ability to establish local monitoring, logging, patching, backup, and recovery processes for on-premises and disconnected environments.
• Strong analytical, troubleshooting and problem-solving skills, with the ability to work independently on complex assignments.
• Effective written and verbal communication skills, including the ability to present technical recommendations clearly to management and non-technical stakeholders.
• Adhere to policies, procedures, standards, codes and regulations relevant to assignments.
• Demonstrate in-depth knowledge of AEP infrastructure, environment and components to enable efficient, comprehensive responses to projects and problems.
• Participate in on-call rotation, after-hours maintenance windows, and storm/emergency response support as required.

Nice To Haves

• Architecture experience including designing end-to-end cloud solutions and influencing platform direction is highly desirable.
• Direct experience building or operating AI/ML or GenAI workloads across cloud or on-premises infrastructure (Amazon Bedrock, SageMaker, vector databases, retrieval-augmented generation patterns, GPU-based training or inference, edge AI, or disconnected environments).
• Experience with FinOps tooling and practices for high-variability workloads.
• Experience in a regulated industry (utility, energy, financial services) or with FedRAMP/GovCloud environments.
• Familiarity with multi-cloud environments (Azure, OCI, GCP) and enterprise data platforms such as Snowflake.
• Certifications: AWS Solutions Architect – Associate or Professional; AWS Certified Machine Learning; Certified Kubernetes Administrator (CKA).

Responsibilities

• Design, build and maintain the AWS account structure, landing zones, and reference patterns that host AI and machine learning workloads, leveraging AWS Control Tower, Landing Zone Accelerator, and Terraform.
• Engineer the compute, storage, and networking foundations required for AI workloads, including GPU and accelerated instance families, high-throughput storage, and data access paths to enterprise data platforms.
• Enable and operate AI platform services such as Amazon Bedrock and SageMaker, including private connectivity, model access provisioning, logging, and quota management.
• Build reusable infrastructure-as-code modules, templates and pipelines so that AI teams can deploy quickly within approved guardrails.
• Design, build and maintain on-premises AI infrastructure for edge and specialized use cases.
• Support AI in disconnected or intermittently connected environments, including local compute, model distribution, patching, monitoring, backup, and recovery.
• Create deployment standards, automation, runbooks, and support processes for on-premises and edge AI.
• Integrate on-premises AI with enterprise identity, security, networking, monitoring, and governance where feasible.
• Partner with Cybersecurity, Enterprise Architecture and Compliance to align AI infrastructure with AEP security standards, regulatory obligations, and responsible AI guardrails.
• Review and remediate configuration drift, vulnerabilities and audit findings across the AI cloud estate; support evidence requests and control attestations.
• Contribute to onboarding and intake processes for new AI use cases, ensuring workloads are provisioned into the right accounts with the right controls from day one.
• Establish and operate FinOps practices for AI workloads, including tagging standards, showback/chargeback, budgets, anomaly detection, and forecasting.
• Analyze and optimize spend on GPU compute, inference and token consumption, storage, and data transfer; recommend commitment strategies such as Savings Plans and Reserved Instances.
• Provide cost transparency and consumption reporting to business stakeholders and technology leadership, and identify optimization opportunities before they become budget issues.
• Partner with monitoring team to create logging, alerting and dashboards for AI infrastructure and workloads.
• Define service-level objectives and operational thresholds for AI platforms, including model endpoint availability, latency, throughput, and error rates.
• Support incident response, root cause analysis and problem management for AI-related infrastructure events; drive preventive actions and automation to reduce recurrence.
• Perform standard cloud engineering functions across the AEP AWS environment including account provisioning, environment builds, platform upgrades, patching, automation and lifecycle management.
• Design, deploy and operate Kubernetes environments (Amazon EKS, ROSA/OpenShift) including cluster architecture, autoscaling, node group and GPU scheduling, ingress, service mesh, RBAC, and cluster security hardening.
• Engineer and support platform services including load balancing, traffic management, API gateways, and integration patterns across cloud, on-premises, edge, and disconnected environments.
• Apply networking fundamentals, VPC design, subnetting, routing, Transit Gateway, Direct Connect, VPN, firewalls, TLS and certificate management to deliver secure, performant connectivity for AI and general workloads.
• Prepare cost estimates, justifications, alternative solutions and technical recommendations; produce technical documentation, runbooks and standards.
• Collaborate with Project Managers, Architects, Solution Engineers, Business Analysts and vendor partners to deliver consistent, reliable solutions that leverage AEP's technology standards, architectures and best practices.
• Adhere to and advocate for change, incident and problem management processes; participate in on-call rotation and after-hours support as required.
• Provide training, mentoring and technical work direction to other engineers on the team.

Benefits

• Base salary
• Annual bonus
• Long-term incentive
• 401(k) match
• AEP Pension
• Comprehensive benefits package designed to support and enhance the overall well-being of employees.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior