Platform Engineer, AI Infrastructure
About The Position
Focused is hiring a Platform Engineer to help build an enterprise AI platform for a global enterprise client. The engineer will work alongside a Senior Platform Engineer on the Kubernetes and cloud foundation while also contributing directly to AI platform work, including wiring up model access, deploying agents, and helping application teams bring LLM workloads into production. The role combines platform and infrastructure engineering, AI platform operations, and AI engineering. It includes building and operating Kubernetes clusters on Azure AKS and GCP GKE; maintaining Terraform modules and deployment pipelines; operating services that support LLM applications; supporting GPU workloads; and developing reference agents and application templates. The position requires working from Focused's Chicago office three days per week and up to 20% travel within the United States. Focused cannot sponsor or take over sponsorship of employment visas for this role.
Requirements
- At least 5 years in platform, infrastructure, DevOps, or SRE roles.
- Hands-on production experience with Kubernetes.
- Experience with Azure and/or GCP.
- Experience with Terraform or similar infrastructure-as-code tools.
- Experience supporting AI/ML platforms or ML workloads in production.
- Proficiency in Python and solid scripting skills.
Nice To Haves
- Experience building LLM applications or agents using LangChain, LangGraph, LlamaIndex, or similar tools.
- Experience with Azure OpenAI or Vertex AI.
- Familiarity with GPU infrastructure and model serving.
- Consulting or client-facing experience.
- Experience shipping an LLM feature or agent to real users, rather than only creating a demo.
- Enjoyment of pairing and learning quickly from senior engineers.
- Ability to build documentation systems that auto-update for the next person and coding agent.
Responsibilities
- Build and operate Kubernetes clusters on Azure AKS and GCP GKE.
- Write and maintain Terraform modules for cloud resources and environments.
- Build CI/CD and GitOps pipelines for platform and application deployments.
- Monitor, troubleshoot, and improve the reliability of AI/ML platform services.
- Deploy and operate supporting services for LLM applications, including model gateways, vector databases, tracing, and evaluation.
- Support GPU workloads, including node pools, scheduling, and cost monitoring.
- Write runbooks and automate day-two operations.
- Build reference agents and LLM application templates that client teams can use.
- Help application teams integrate with the platform and debug their workloads.
- Prototype with agent frameworks and LLM APIs to test platform requirements.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
