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Salary: USD 201,300 - 352,300 / annual
ServiceNow is seeking a Staff AI-Native DevOps Engineer to lead a new DevOps practice within the Conversational AI team. You will own the build, deployment, and reliability of the systems behind ServiceNow Otto, the company's conversational AI platform, from commit through production across SaaS environments.
Your responsibilities will span CI/CD pipelines, release automation, infrastructure on Kubernetes, 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 a 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:
- Designing, building, and operating scalable, reliable, and secure cloud-native applications and infrastructure
- 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 with root cause analysis
- Architecting and optimizing cloud infrastructure across public and private cloud environments
- Leading complex infrastructure initiatives and establishing engineering best practices across teams
- Leveraging AI tools and automation to accelerate development and improve operational efficiency
- Deploying, operating, and troubleshooting AI-powered services in production, including model API integrations, observability, latency, and resource utilization
Requirements:
- 8+ years of professional software engineering, infrastructure engineering, DevOps, or related experience
- 6+ years of experience deploying and operating containerized applications and distributed services in production environments
- 4+ years of hands-on experience with Kubernetes, cloud infrastructure, and infrastructure automation
- Proficiency in at least one programming or scripting language (Go, Python, Bash) with experience building production-grade automation and tooling
- Deep expertise in designing and operating scalable, reliable, and secure cloud-native applications
- Strong software engineering skills with experience developing infrastructure tooling
- Experience leading complex infrastructure initiatives and driving adoption across multiple teams
- Strong problem-solving skills for diagnosing complex distributed systems issues
- Strong understanding of modern AI technologies including LLMs, Generative AI, and agentic architectures
- Practical experience using AI-assisted development tools (GitHub Copilot, Cursor) with ability to validate and safely integrate AI-generated code
- Working knowledge of LLMs, Generative AI, and agentic AI concepts, including integration patterns and operational considerations
- AI-assisted debugging and trace analysis with understanding of data privacy rules
- Understanding of LLM-powered application architectures (model inference, RAG, tool calling, agentic workflows)
Preferred qualifications:
- 12+ years of experience 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 driving cross-team infrastructure initiatives
- 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
