Infrastructure Engineer Lead – Cloud AI

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 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 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…