Senior AI Cloud Infra Architect (0→1) | Flexible Location

Emerald AI; United States; Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Competitive pay + equity

Comprehensive benefits (medical,Dental

Flexible location with WFH days

Job summary

Emerald AI, a pioneering AI data center platform, is hiring a senior infrastructure engineer to architect and stand up our managed cloud services from end to end. You will shape the platform, control plane, and customer experience, owning major design decisions and implementing them personally.

The right candidate has built or led a core managed cloud or AI platform at scale—GPU cloud, hyperscaler, HPC center—and excels at architectural work while still coding.

Qualifications

• 7+ years in infrastructure or platform engineering for production cloud/AI platforms.
• Kubernetes and Slurm experience offered as managed service.
• Experience deploying or operating Lustre or parallel FS like GPFS/Weka/VAST.
• Strong understanding of cloud service fundamentals, APIs, quotas, and metering.

Responsibilities

• Architect managed services from 0→1 for GPU capacity with scalable productization.
• Build core platform: control plane, self-service interfaces, and lifecycle metering.
• Onboard and evaluate infrastructure partners and vendor capabilities.
• Design end-to-end multi-tenancy with secure boundaries and QoS.
• Oversee Kubernetes/Slurm for large-scale training and inference workloads.
• Lead storage strategy with Lustre/VAST/Weka integrations.
• Define SLOs and incident response aligned with provider SLAs.

Skills

Kubernetes

Slurm

Linux systems

Infrastructure as code

Python

Go

InfiniBand networking

Cloud fundamentals

Tools

Lustre

GPFS / BeeGFS

VAST / Weka

Terraform

Ansible

Job description

Emerald AI, a pioneering AI data center platform, is hiring a senior infrastructure engineer to architect and stand up our managed cloud services from end to end. You will shape the platform, control plane, and customer experience, owning major design decisions and implementing them personally.

The right candidate has built or led a core managed cloud or AI platform at scale—GPU cloud, hyperscaler, HPC center—and excels at architectural work while still coding.