Senior Platform Engineer, AI Infrastructure
About The Position
Focused is seeking 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 that client teams use to deliver LLM applications and agents, including Kubernetes, GPU capacity, security, and operational guardrails. The role sets technical direction and mentors a Platform Engineer working on the engagement. The position combines AI platform architecture, Kubernetes and cloud infrastructure, security and governance, and client-facing technical leadership. The engineer will help the client’s team take ownership of the resulting platform and operate it beyond the engagement. The role requires working from Focused’s Denver 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 Denver base salary range is $180,000–$220,000.
Requirements
- At least 10 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 experience.
- Experience running GPU infrastructure, such as NVIDIA GPU Operator, node pools, and scheduling, or similar.
- Experience building infrastructure that supports production LLM workloads.
- 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.
- A record of building a platform that other engineering teams chose to adopt.
- Experience meaningfully reducing GPU spending without slowing teams down.
- Ability to explain a 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.
- Establish 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 that remain useful after the engagement.
- Build trust with client engineering leaders and leave the client team able to run the platform.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
