Senior AI-Native DevOps Engineer for Production Platform

Devopsroles; Santa Clara (CA); On-site; USD 176,000 - 308,000; Full time

Benefits offered by this job

Health plans

401(k) plan with company match

ESPP

Matching donations

Flexible time away

Job summary

ServiceNow is seeking a Staff AI-Native DevOps Engineer to join a new DevOps practice within the Conversational AI team. The role covers CI/CD, release automation, and Kubernetes-based infrastructure, with a focus on AI-native delivery and reliability across SaaS environments.

You will partner with scientists and engineers to drive secure, scalable systems. Responsibilities include building automation tools, observability, and championing best practices across teams, leveraging ML/LLM insights

Qualifications

• 8+ years of professional software or infrastructure engineering experience.
• 6+ years deploying containerized applications in production.
• 4+ years with Kubernetes, cloud infrastructure, and IaC tooling.
• Proficiency in Go, Python, or Bash with automation experience.
• Experience using AI-assisted development tools in workflows.

Responsibilities

• Deploy, scale, and operate distributed microservices in production Kubernetes environments.
• Design infrastructure automation using IaC and Kubernetes-native tooling.
• Build and maintain CI/CD pipelines and GitOps workflows.
• Implement observability and incident response practices to improve reliability.
• Architect cloud infra across public and private clouds with security and cost focus.

Skills

Cloud-native infra

Go / Python

Kubernetes

CI/CD pipelines

Observability

Tools

Terraform

Ansible

Helm

Kubernetes Operators

Jenkins

GitHub Actions

Tekton

ArgoCD

Job description

ServiceNow is seeking a Staff AI-Native DevOps Engineer to join a new DevOps practice within the Conversational AI team. The role covers CI/CD, release automation, and Kubernetes-based infrastructure, with a focus on AI-native delivery and reliability across SaaS environments.

You will partner with scientists and engineers to drive secure, scalable systems. Responsibilities include building automation tools, observability, and championing best practices across teams, leveraging ML/LLM insights