About the opportunity
- The organization we deliver outcome-based cloud solutions that help customers modernize applications, launch new products, and run complex cloud environments. RackAI is our strategic platform for bringing secure, scalable, cloud-native AI capabilities to customers and internal teams alike.
- We're looking for an expert
Software Developer III
to join the PVC Product Software Engineering team and help design and evolve the core of RackAI. You'll pair deep
Go
and
Kubernetes
expertise with genuine architectural leadership — and you'll help set the standard for how a modern engineering team builds software in the age of AI. It's a hands-on senior role: strategic enough to shape direction, close enough to the code to lead by example.
- Put simply:
we build AI, and we build with AI.
If you want to work at the intersection of cloud-native platform engineering and applied AI — and help define an AI-first engineering culture — this is the seat.
Why RackAI
- You'll help shape the future of AI-powered products at the organization — building with modern cloud-native technology, solving hard problems at scale, and setting the standard for how engineering teams work in an AI-first world.
What you'll do
- Design and develop highly scalable backend services in
Go (Golang)
.
- Build Kubernetes-native applications — operators, controllers, and platform services — using controller-runtime and kubebuilder.
- Define and evolve the RackAI platform architecture for growth, reliability, security, and operational excellence.
- Develop the APIs, automation frameworks, and platform capabilities that power AI-enabled products.
- Champion cloud-native best practices: observability, resiliency, and operational readiness.
Lead an AI-first engineering culture
- Make AI-assisted engineering the default way of working — champion an
AI-driven, AI-first
approach across the team.
- Embed AI development tooling —
Claude Code
,
Kiro
,
Codex
, or
GitHub Copilot
— across the lifecycle, from design and coding through testing, review, and documentation.
- Set the standards and guardrails for responsible, effective use of AI coding assistants — code quality, security, licensing, and human-in-the-loop review.
- Turn AI augmentation into measurable gains in velocity and quality through prompt engineering and agentic workflows.
- Coach engineers to get real leverage from AI tooling, and continually assess what to adopt next.
