Senior Software Engineer - Enterprise AI

Rubrik is on a mission to secure the world’s data. With our Zero Trust Data Security™ platform, we help organizations achieve business resilience against cyberattacks, malicious insiders, and operational disruptions. Rubrik protects data wherever it lives—across enterprise, cloud, and SaaS—and delivers data observability, orchestration, and recovery at scale.

The Information Technology team at Rubrik influences business processes, employee experience, and technologies to scale our organization to $1B+. This team creates operational efficiency across the company by centralizing the management of Infrastructure, Technology, and Data. The IT team ensures all stages of the software development lifecycle in a secured environment and scrutinizes the deployment of proper processes along with governance. They champion Rubrik on Rubrik and are the first customers of the Engineering teams at Rubrik. We're transforming global infrastructure, and we’re looking for bold thinkers and builders to join us. If you're passionate about making an impact and shaping the future of cloud and platform infrastructure—we'd love to hear from you.

The Enterprise AI team is Rubrik’s internal AI enablement engine. We evaluate where AI can make a real difference, build the platforms and patterns that make adoption easy, and help engineering teams across the organization work smarter and faster. We operate at the intersection of applied AI, distributed systems, and enterprise operations — our job is to make Rubrik more efficient, one AI-powered workflow at a time.
This is a high-impact, high-autonomy team where you’ll work closely with engineering, product and operations teams across Rubrik to bring AI capabilities to life.

We’re hiring a Sr. Software Engineer to design, build, and operate the platform and backend systems that power Rubrik at scale. You’ll own core infrastructure — from Kubernetes and cloud-native services to APIs and developer tooling — and work closely with product, AI, and security teams. This is a hands-on, high-ownership role where you write production code, design systems, lead code reviews, and make the engineering organization more effective.

• Own end-to-end delivery of major platform initiatives, from design through deployment and post-launch success.
• Own Kubernetes at depth — clusters, networking, operators, container lifecycle, and multi-tenant orchestration.
• Design, develop, and optimize distributed services and cloud-native infrastructure on AWS and/or GCP for scale, reliability, and performance.
• Drive engineering excellence through code quality standards, design reviews, automation, and CI/CD best practices.
• Collaborate across teams — Product, AI, and Security — to align architecture with business objectives.
• Be a mentor and multiplier, guiding engineers through architecture decisions, trade-offs, and delivery.
• Partner with leadership to align engineering strategy with product objectives and technical roadmap.

Required

• 9+ years of software engineering with deep backend and infrastructure focus.
• Strong programming skills in Python and/or Go — you ship production code, not just scripts and configs.
• Deep, hands-on Kubernetes experience — building and operating clusters, not just deploying to them.
• Proven experience designing and operating distributed systems in production.
• Cloud-native fluency across AWS and/or GCP — compute, storage, IAM, networking and managed services.
• Experience with infrastructure-as-code (Terraform or similar) and CI/CD pipelines.
• Familiarity with applied AI tooling and patterns — agentic AI tools (Claude, LiteLLM), AI gateways, agent frameworks — and being able to build backend services that integrate with them.
• Strong system design and architectural judgment.
• Clear communicator who partners well across product, security, and AI teams.

Preferred

• Observability stacks — Prometheus, Grafana, Datadog, OpenTelemetry.
• Multi-cloud or hybrid infrastructure experience (AWS, GCP, on-prem).
• Familiarity with API gateways, AI gateways, and policy/authorization frameworks (ABAC,
  OPA).
• Service mesh or platform-as-a-service design experience.
• Track record of improving engineering productivity at scale.