Infrastructure Engineer Lead – Cloud AI

American Electric Power•Gahanna, OH

2d•$136,539 - $177,503•Remote

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. The role covers cloud foundations, networking, containers, compute, identity, cost management, and observability, and supports core cloud engineering services. The engineer partners with AI, architecture, cybersecurity, network, and application teams to deliver secure, cost-effective, production-ready infrastructure. The position designs and maintains AWS account structures, landing zones, and infrastructure-as-code patterns for AI workloads, and builds compute, storage, and networking foundations, including GPU and accelerated instances. It enables and operates services such as Amazon Bedrock and SageMaker, and supports on-premises, edge, disconnected, and intermittently connected AI environments with deployment standards, automation, patching, monitoring, backup, and recovery. The role supports security, compliance, and governance; establishes FinOps practices and cost transparency; and partners with monitoring teams to define observability, service-level objectives, and operational thresholds. It also performs core AWS engineering, operates Kubernetes environments including EKS and ROSA/OpenShift, engineers connectivity and platform services, documents standards and runbooks, collaborates across delivery teams, participates in operational processes and on-call support, and provides training and technical direction to other engineers. A bachelor's degree in computer science, engineering, or a related technical field is required, along with 12 years of relevant work experience. An equivalent combination of education and related experience may be considered. The position offers a compensation range of $136,539 to $177,503. Work location is flexible but must be an approved location and is subject to vetting and final approval. Relocation to an approved work location may be considered at the candidate's expense.

Requirements

Demonstrated hands-on AWS engineering experience, including IAM, VPC networking, compute, storage, encryption/KMS, and account and organization structure.
Strong Kubernetes expertise in 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 is required. Ansible, Python, or PowerShell are preferred.
Experience with CI/CD pipelines.
Working knowledge of cloud security principles, identity and access management, and compliance requirements.
Experience implementing cloud monitoring and cost management.
Ability to establish local monitoring, logging, patching, backup, and recovery processes for on-premises and disconnected environments.
Strong analytical, troubleshooting, and problem-solving skills, including the ability to work independently on complex assignments.
Effective written and verbal communication skills, including presenting technical recommendations clearly to management and non-technical stakeholders.
Bachelor's degree in computer science, engineering, or a related technical field; an equivalent combination of education and related experience may be considered.
12 years of relevant work experience; an equivalent combination of education and related experience may be considered.
Ability to work from an approved work location; remote locations require vetting and final approval. Candidates may relocate to an approved location at their own expense if relocation meets staffing needs.

Nice To Haves

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

Responsibilities

Design, build, and maintain AWS account structures, landing zones, and reference patterns for AI and machine learning workloads using AWS Control Tower, Landing Zone Accelerator, and Terraform.
Engineer compute, storage, and networking foundations for AI workloads, including GPU and accelerated instances, 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 for AI teams to deploy 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 infrastructure with security standards, regulatory obligations, and responsible AI guardrails.
Review and remediate configuration drift, vulnerabilities, and audit findings; support evidence requests and control attestations.
Contribute to onboarding and intake processes for AI use cases, ensuring workloads are provisioned into appropriate accounts with controls.
Establish and operate FinOps practices for AI workloads, including tagging, showback/chargeback, budgets, anomaly detection, and forecasting.
Analyze and optimize GPU compute, inference and token consumption, storage, and data-transfer costs; recommend Savings Plans, Reserved Instances, and other commitment strategies.
Provide cost transparency and consumption reporting to stakeholders and leadership, and identify optimization opportunities.
Partner with monitoring teams to create logging, alerting, and dashboards for AI infrastructure and workloads.
Define service-level objectives and operational thresholds for AI platforms, including endpoint availability, latency, throughput, and error rates.
Support incident response, root cause analysis, and problem management for AI infrastructure events; drive preventive actions and automation.
Perform cloud engineering across the AWS environment, including account provisioning, environment builds, platform upgrades, patching, automation, and lifecycle management.
Design, deploy, and operate Kubernetes environments, including Amazon EKS and ROSA/OpenShift, covering cluster architecture, autoscaling, node groups, GPU scheduling, ingress, service mesh, RBAC, and security hardening.
Engineer and support platform services including load balancing, traffic management, API gateways, and integrations across cloud, on-premises, edge, and disconnected environments.
Apply networking fundamentals, including VPC design, subnetting, routing, Transit Gateway, Direct Connect, VPN, firewalls, TLS, and certificate management.
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 solutions aligned with technology standards and best practices.
Follow 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.
Adhere to policies, procedures, standards, codes, and regulations relevant to assignments.
Develop in-depth knowledge of AEP infrastructure, environments, and components to support projects and resolve problems.
Participate in on-call rotation, after-hours maintenance windows, and storm or emergency response support as required.