Senior Platform Engineer, AI Infrastructure

Focused•Chicago, IL

1d•$185,000 - $225,000•Hybrid

About The Position

Focused is hiring a Senior Platform Engineer to lead the build-out of an enterprise AI platform for a global enterprise client. The engineer will design the foundation client teams use to ship LLM applications and agents, including Kubernetes, GPU capacity, security, and guardrails that support hundreds of engineers. The role sets technical direction and mentors a Platform Engineer working on the engagement. The position includes designing a multi-tenant platform for LLM and agentic AI workloads; architecting GPU infrastructure for inference and fine-tuning; establishing shared agent services; and operating production Kubernetes across Azure AKS and GCP GKE. The role also owns Terraform modules and infrastructure-as-code standards, CI/CD and GitOps workflows, security and governance controls, and collaboration with security and compliance teams. As a client-facing technical leader, this engineer will lead architecture reviews, document decisions, build relationships with client engineering leaders, and help the client team run the resulting platform. The role requires working in the Chicago office three days per week and up to 20% travel within the United States. Focused cannot sponsor or take over sponsorship of an employment visa at this time. The Chicago base salary range is $185,000–$225,000.

Requirements

10 or more years of experience in infrastructure, platform, or DevOps engineering.
Deep production experience with Kubernetes, including Azure AKS.
Hands-on experience with GCP.
Expert-level Terraform skills.
Experience running GPU infrastructure, such as NVIDIA GPU Operator, node pools, or scheduling.
Experience building infrastructure that supports LLM workloads in production.
Strong cloud security background, including IAM, networking, secrets, and policy as code.
Experience leading technical direction for a team.

Nice To Haves

Experience building an internal agentic AI platform.
Familiarity with model serving technologies such as vLLM, Triton, KServe, or Ray Serve.
Experience with Azure OpenAI or Vertex AI.
Consulting or client-facing experience.
Experience building a platform that other engineering teams chose to adopt.
Experience meaningfully reducing GPU spend without slowing teams down.
Ability to explain platform architecture to both a CTO and a junior engineer.

Responsibilities

Design and build a multi-tenant platform for LLM and agentic AI workloads, from model access through deployment.
Architect GPU infrastructure for inference and fine-tuning, including scheduling, autoscaling, and cost controls.
Establish shared agent services, including model gateways, secrets, tracing, evaluation, and deployment pipelines.
Make build-versus-buy decisions and explain tradeoffs to technical and executive stakeholders.
Architect and operate production Kubernetes on Azure AKS and GCP GKE.
Own Terraform modules and infrastructure-as-code standards across environments.
Build CI/CD and GitOps workflows that make platform changes safe and repeatable.
Design identity, network isolation, and secrets management for AI workloads.
Implement guardrails for data access, model usage, and auditability.
Partner with security and compliance teams to meet enterprise requirements.
Mentor and direct a Platform Engineer on the engagement.
Lead architecture reviews and write decision records.
Build trust with client engineering leaders and leave the client team able to operate the platform.