Senior Platform Engineer, AI Infrastructure

Focused
Chicago, IL
On-site

Who this role is best for

Geared toward infrastructure veterans comfortable with AI platform architecture, this role suits candidates with deep Kubernetes and cloud security experience who thrive in client-facing, collaborative environments in Chicago.

Best fit for

  • Candidates with 10+ years in infrastructure or DevOps and a track record of building enterprise platforms
    — “10+ years in infrastructure, platform, or DevOps engineering”
  • Individuals with hands-on experience in GPU infrastructure and model serving for LLM workloads
    — “Experience running GPU infrastructure (NVIDIA GPU Operator, node pools, scheduling, or similar)”
  • Candidates who have led technical direction and mentored others in complex platform projects
    — “Experience leading technical direction for a team”

Things to consider

  • This role requires being in the Chicago office 3 days per week and up to 20% travel within the US
    — “This role will require being in the Chicago office 3 days per week and up to 20% travel within the United States”
  • Visa sponsorship is not available, which may limit eligibility to US-based candidates
    — “Focused is unable to sponsor or take over sponsorship of the employment Visa process at this time”

How to stand out

  • Emphasize experience in multi-tenant AI platform design and LLM deployment
    — “Design and build a multi-tenant platform for LLM and agentic AI workloads”
  • Highlight your ability to explain complex architectures to both executives and junior engineers
    — “You can explain a platform architecture to a CTO and a junior engineer in the same meeting”
  • Showcase your expertise in Kubernetes with Azure AKS and GCP GKE in production settings
    — “Architect and run production Kubernetes on Azure AKS and GCP GKE”
  • Demonstrate your ability to cut GPU costs while maintaining team productivity
    — “You've cut GPU spend meaningfully without slowing teams down”
  • Include examples of building infrastructure for LLM workloads and implementing security guardrails
    — “Experience building infrastructure that supports LLM workloads in production”
Pace · SteadyCollaboration · HighAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • design and build a multi-tenant platform for LLM and agentic AI workloads
  • architect GPU infrastructure
  • build CI/CD and GitOps workflows
Typical background
10+ years in infrastructure, platform, or DevOps engineering

Skills & requirements

Required

KubernetesTerraformCloud InfrastructureSecurityAI Infrastructure

Preferred

Azure AKSGCP GKEModel Serving

Stack & domain

KubernetesAzure AKSGCP GKETerraformGPU InfrastructureLLM WorkloadsCloud SecurityIAMNetworkingSecrets ManagementData AccessModel UsageAuditabilityLeadershipConsultingClient-facingAIInfrastructurePlatform Engineering

About the role

Original posting from Focused via Greenhouse

Who we are:

At Focused, we move quickly to deliver quality software that achieves client outcomes and meets their customer's needs. We strategically partner with our clients to leverage our expertise in design and software, while our clients bring their own domain expertise. We work with a variety of clients from different industries, collaborating as we get new products to market, modernizing legacy systems, or helping teams learn the skills they need to be successful.

Our values:

Listen first • We are experts in product practices but lifelong learners in the domain of our customers. We research, collaborate, and understand.

Learn why • We ask questions and talk to users to understand problem spaces, objectives, and goals, which allows us to deeply invest and drive towards the outcomes of our clients.

Love your craft • We love diving into a variety of domains and solving problems. We take pride in delivering value, in communicating progress, and guiding our clients to success.

We're looking for a Senior Platform Engineer to lead the build-out of an enterprise AI platform for a global enterprise client. You'll design the foundation their teams use to ship LLM applications and agents: Kubernetes, GPU capacity, security, and the guardrails that let hundreds of engineers move fast without breaking things. You'll set technical direction and mentor a Platform Engineer working alongside you.

Key Responsibilities:

AI Platform Architecture

Design and build a multi-tenant platform for LLM and agentic AI workloads, from model access to deployment

Architect GPU infrastructure for inference and fine-tuning, including scheduling, autoscaling, and cost controls

Stand up shared services for agents: model gateways, secrets, tracing, evaluation, and deployment pipelines

Make build-vs-buy calls and explain the tradeoffs to technical and executive stakeholders

Kubernetes & Cloud Infrastructure

Architect and run production Kubernetes on Azure AKS and GCP GKE

Own Terraform modules and IaC standards across environments

Build CI/CD and GitOps workflows that make platform changes safe and repeatable

Security & Governance

Design identity, network isolation, and secrets management for AI workloads

Put guardrails in place for data access, model usage, and auditability

Partner with security and compliance teams to meet enterprise requirements

Leadership & Consulting

Mentor and direct a Platform Engineer on the engagement

Lead architecture reviews and write the decision records that outlast the engagement

Build trust with client engineering leaders and leave their team able to run what we build

Required Qualifications:

10+ years in infrastructure, platform, or DevOps engineering

Deep production experience with Kubernetes, including Azure AKS

Hands-on experience with GCP

Expert-level Terraform

Experience running GPU infrastructure (NVIDIA GPU Operator, node pools, scheduling, or similar)

Experience building infrastructure that supports LLM workloads in production

Strong cloud security background: IAM, networking, secrets, policy as code

Experience leading technical direction for a team

Preferred Qualifications (Exceptional Candidates):

Experience building an internal agentic AI platform

Familiarity with model serving (vLLM, Triton, KServe, Ray Serve)

Experience with Azure OpenAI or Vertex AI

Consulting or client-facing experience

What Sets Exceptional Candidates Apart:

You've built a platform other engineering teams chose to adopt

You've cut GPU spend meaningfully without slowing teams down

You can explain a platform architecture to a CTO and a junior engineer in the same meeting

What to know before you apply:

This role will require being in the Chicago office 3 days per week and up to 20% travel within the United States.

Focused is unable to sponsor or take over sponsorship of the employment Visa process at this time.

The Chicago base salary range for this role is $185,000 - $225,000.

Source: Focused careers (Greenhouse)

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