Infrastructure Engineer Lead – Cloud AI

Job Posting End Date 11-16-2026 Please note the job posting will close on the day before the posting end date Job Summary 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. Job Description What you’ll do: AI Cloud Foundations • 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 case. • 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. Controls, Security & Governance • 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. FinOps & Cost Management • 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. Monitoring & Observability • 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. Core Cloud Engineering & Platform Services • 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. Required Skills & Experience • 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 o