AI Fabric Field Engineer: Deployment & Automation

Upscale; Kentucky; On-site; USD 120,000 - 180,000; Full time

Job summary

Upscale, Inc. is building high-performance AI infrastructure and delivering Professional Services to deploy and support customer environments.

As a Forward-Deployed Engineer, you will work directly with customers to deploy, validate, and stabilize AI fabric environments, build lab setups, and develop runbooks for automated operations. You will collaborate with engineering, product, and sales, driving technical outcomes, crafting deployment guides, and contributing to a scalable PS practice

Qualifications

• 8+ years in customer-facing escalation, field, or site reliability engineering.
• Experience deploying and supporting products in customer environments across private cloud, public cloud, and virtualization.
• Hands-on experience with production container platforms: Kubernetes, Docker, and CI/CD (GitLab, Jenkins).

Responsibilities

• Forward-deployed engineering: deploy, bring-up, and escalate to engineering with remediation.
• Professional Services: scoped, repeatable deployments, health checks, and UP testing.
• Lab validation: reproduce issues, validate fixes, and run UAT.
• Runbooks and automation: build/runbooks and automation data for AI monitoring agent.
• Enablement: provide technical guidance and documentation for field and sales teams.

Skills

Customer-facing communication

Linux troubleshooting

Multi-cloud expertise

Documentation

SRE/Operations mindset

Python scripting

Tools

Kubernetes

Docker

GitLab

Jenkins

Terraform

Python

Bash

Prometheus

Grafana

Job description

Upscale, Inc. is building high-performance AI infrastructure and delivering Professional Services to deploy and support customer environments.

As a Forward-Deployed Engineer, you will work directly with customers to deploy, validate, and stabilize AI fabric environments, build lab setups, and develop runbooks for automated operations. You will collaborate with engineering, product, and sales, driving technical outcomes, crafting deployment guides, and contributing to a scalable PS practice