Infrastructure Engineer Lead – Cloud AI

11-16-2026

Please note the job posting will close on the day before the posting end date

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.

• 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 Sage Maker, 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.

• 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 Fin Ops 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/Open Shift) 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,…