Platform Engineer, AI Infrastructure
About The Position
Focused is seeking a Platform Engineer to help build an enterprise AI platform for a global enterprise client. The role works alongside a Senior Platform Engineer on Kubernetes and cloud foundations, while also supporting AI platform development, including connecting model access, deploying agents, and helping application teams bring LLM workloads into production. The position combines infrastructure engineering with hands-on AI platform and application work. The role requires working in the Chicago office three days per week and up to 20% travel within the United States. Focused is unable to sponsor or take over sponsorship of employment visas at this time. The Chicago base salary range is $145,000–$175,000.
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 with 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.
- Shipped an LLM feature or agent to real users, not just a demo.
- Enjoys pairing and learns quickly from senior engineers.
- Builds 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 for client teams to use.
- Help application teams integrate with the platform and debug their workloads.
- Prototype with agent frameworks and LLM APIs to test what the platform needs to support.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
