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Salary: USD 176,100 - 308,200 / annual
ServiceNow is seeking a Staff AI-Native DevOps Engineer to join the Conversational AI team, which builds the core platform behind ServiceNow Otto, their conversational AI experience. This is a new DevOps practice role focused on owning the build, deployment, and reliability of systems powering Otto across SaaS environments.
You will be responsible for the full stack from commit through production: CI/CD pipelines, release automation, Kubernetes infrastructure, observability, and operational tooling that enables agent orchestration, MCP and A2A services, and the central caching layer to ship safely and frequently. You will partner closely with applied scientists and software engineers to make AI-native delivery repeatable, measurable, and secure.
The role combines deep expertise in cloud infrastructure, containerization, orchestration, and infrastructure as code with practical understanding of how ML and LLM-based services behave in production. You will drive pragmatic engineering with measurable impact on deployment velocity and service reliability.
Key responsibilities include: deploying, scaling, and operating distributed microservices in production Kubernetes environments; designing and implementing infrastructure automation using IaC and Kubernetes-native tooling (Terraform, Ansible, Helm, Kubernetes Operators); building and maintaining CI/CD pipelines and GitOps workflows (Jenkins, GitHub Actions, Tekton, ArgoCD); implementing observability, monitoring, and incident response practices; and architecting cloud infrastructure across public and private environments with emphasis on security, scalability, and cost efficiency.
AI skills are critical at this level. You will confidently use AI coding assistants (GitHub Copilot, Cursor) with rigorous code review; perform AI-assisted debugging and trace analysis with clear understanding of data privacy; understand LLM-powered application architectures including model inference, RAG, tool calling, and agentic workflows; and deploy, operate, and troubleshoot AI-powered services in production.
REQUIREMENTS:
Minimum:
- 8+ years professional software engineering, infrastructure engineering, DevOps, or related experience
- 6+ years deploying and operating containerized applications and distributed services in production
- 4+ years hands-on experience with Kubernetes, cloud infrastructure, and infrastructure automation
- Proficiency in Go, Python, Bash, or similar languages with production-grade automation and tooling experience
- Practical experience using AI-assisted development tools with demonstrated ability to validate, debug, and safely integrate AI-generated code
- Working knowledge of LLMs, Generative AI, and agentic AI concepts including integration patterns and operational considerations
Preferred:
- 8+ years in software engineering, infrastructure engineering, DevOps, or platform engineering
- Extensive experience architecting and operating Kubernetes-based SaaS platforms at scale
- Experience developing Kubernetes Operators, custom controllers, or Kubernetes-native automation
- Demonstrated technical leadership in cross-team infrastructure initiatives and platform engineering practices
- Hands-on experience deploying, integrating, or operating LLM-powered applications or agentic systems in production
- Experience building AI-enabled infrastructure automation, observability, incident diagnosis, or remediation workflows
