You’ll help define the systems, tooling, and operational standards that shape how the company deploys software, runs AI workloads, and scales globally.

You’ll work on high-impact systems: Kubernetes platforms, multi-cloud deployment pipelines, autoscaling infrastructure, distributed model serving, secure networking, and developer tooling that makes complex infrastructure easy to operate.

We also care deeply about engineering judgment, especially when it comes to AI tooling. We’re looking for someone who understands where AI can accelerate infrastructure engineering, where it introduces operational risk, and how to build safe workflows around AI-assisted development.

You’ll shape how infrastructure engineering is done here, from architecture decisions, to deployment workflows, to the standards the rest of the engineering organization builds on.

What You’ll Do

This role is about building infrastructure that remains reliable, portable, and understandable as the platform scales.

• Design and maintain cloud infrastructure across Azure and GCP with a strong focus on resilience and portability

• Build and improve Kubernetes-based platforms for running production AI and application workloads

• Create CI/CD systems that deploy consistently across multiple cloud environments

• Implement infrastructure as code using Terraform and modern automation workflows

• Design secure networking foundations including VPCs, ingress, DNS, firewall policies, and private connectivity

• Improve observability across the platform through logging, metrics, tracing, and alerting systems

• Enable autoscaling and workload orchestration using KEDA and Kubernetes-native scaling patterns

• Help design and operate distributed model serving and inference infrastructure

• Define IAM and RBAC strategies that balance developer velocity with strong security boundaries

• Work closely with platform, AI, security and application engineers to improve reliability, security and developer experience

• Help establish engineering standards, operational practices, and long-term infrastructure strategy

• Treat infrastructure as software: versioned, reviewed, automated, reproducible, and observable

  
  

Who You Are

You’re an infrastructure engineer who thinks in systems. You care about operational simplicity, reliability under failure, and building platforms that other engineers love using.

• You’ve built and operated production infrastructure in Azure and GCP environments

• You have strong experience with Terraform and infrastructure automation practices

• You’re comfortable operating Kubernetes clusters and containerized workloads in production

• You have hands-on experience with cloud networking, ingress, DNS, firewall policies, and secure connectivity patterns

• You’ve worked on observability systems and understand what good operational visibility looks like

• You think carefully about reliability, failure modes, and security tradeoffs, not just happy-path deployments

• You have experience and strong opinions about AI coding tools like Claude Code, Cursor, Copilot, or Codex in engineering workflows

• You can clearly articulate when AI-generated infrastructure code is useful, when it creates operational risk, and where human review is non-negotiable

• You understand CI/CD systems and how to build reliable deployment workflows across environments

• You’re pragmatic, highly technical, and capable of balancing speed with long-term maintainability

• You enjoy collaborating with other engineers, mentoring where helpful, and raising the overall engineering bar

• You’ve likely spent 7+ years working in infrastructure, platform engineering, SRE, or distributed systems environments—and you’ve built systems that needed to work reliably at scale.