Staff AI-Native DevOps Engineer

ServiceNow•Santa Clara, CA

About The Position

The Staff AI-Native DevOps Engineer will join a new DevOps practice within ServiceNow’s Conversational AI team, which builds the core platform behind ServiceNow Otto. The role owns the build, deployment, and reliability of systems behind the conversational AI platform, from commit through production across SaaS environments. Work spans CI/CD pipelines, release automation, Kubernetes infrastructure, observability, and operational tooling for agent orchestration, MCP and A2A services, and the central caching layer. The engineer partners with applied scientists and software engineers to make AI-native delivery repeatable, measurable, and secure. The role combines cloud infrastructure, containerization, orchestration, and infrastructure-as-code expertise with a practical understanding of ML- and LLM-based services in production. It requires pragmatic engineering focused on measurable improvements to deployment velocity and service reliability, along with technical leadership, sound engineering practices, and effective use of AI tools and automation.

Requirements

At least 8 years of professional software engineering, infrastructure engineering, DevOps, or related experience.
At least 6 years of experience deploying and operating containerized applications and distributed services in production environments.
At least 4 years of hands-on experience with Kubernetes, cloud infrastructure, and infrastructure automation.
Proficiency in at least one programming or scripting language, such as Go, Python, or Bash, including experience building production-grade automation and tooling.
Practical experience using AI-assisted development tools in software engineering workflows, 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.
Deep expertise designing, building, and operating scalable, reliable, and secure cloud-native applications and infrastructure.
Experience leading complex infrastructure initiatives, establishing engineering best practices, and driving adoption across multiple teams.
Strong problem-solving skills, including diagnosing complex distributed-systems issues and improving performance, reliability, and operational efficiency.
Strong understanding of modern AI technologies, including LLMs, Generative AI, and agentic architectures, and ability to apply them to engineering and operational challenges.
Ability to use AI coding assistants such as GitHub Copilot or Cursor, review generated code for logic errors, security anti-patterns, and edge cases, and understand data privacy rules for prompts.
Understanding of LLM-powered application architectures, including model inference, retrieval-augmented generation (RAG), tool calling, and agentic workflows, and their infrastructure, scalability, and reliability implications.
Experience deploying, scaling, and operating distributed microservices in production Kubernetes environments.
Experience with IaC and Kubernetes-native tooling, including Terraform, Ansible, Helm, and Kubernetes Operators.
Experience building CI/CD pipelines and GitOps workflows, such as with Jenkins, GitHub Actions, Tekton, and ArgoCD.
Experience with observability, monitoring, incident response, root cause analysis, and remediation.

Nice To Haves

At least 8 years of experience in software engineering, infrastructure engineering, DevOps, or platform engineering.
Extensive experience architecting and operating Kubernetes-based SaaS platforms at scale, including multi-service deployments and complex production environments.
Experience developing Kubernetes Operators, custom controllers, or other Kubernetes-native automation to manage application lifecycle and infrastructure operations.
Demonstrated technical leadership driving cross-team infrastructure initiatives, improving production reliability, and establishing scalable platform engineering practices.
Hands-on experience deploying, integrating, or operating LLM-powered applications or agentic systems in production environments.
Experience building AI-enabled infrastructure automation, observability, incident diagnosis, or remediation workflows to improve platform reliability and engineering productivity.

Responsibilities

Own the build, deployment, and reliability of the systems behind ServiceNow Otto across SaaS environments, from commit through production.
Deploy, scale, and operate distributed microservices in production Kubernetes environments, including application lifecycle, networking, security, and service reliability.
Design and implement infrastructure automation using Infrastructure as Code and Kubernetes-native tooling, including Terraform, Ansible, Helm, and Kubernetes Operators.
Build and maintain CI/CD pipelines and GitOps workflows using tools such as Jenkins, GitHub Actions, Tekton, and ArgoCD.
Implement observability, monitoring, and incident response practices; perform root cause analysis and drive remediation to improve availability, fault tolerance, and operational resilience.
Architect and optimize public and private cloud infrastructure for security, scalability, cost efficiency, and operational simplicity.
Develop infrastructure tooling and automation using Go, Python, or similar languages.
Lead complex infrastructure initiatives, establish engineering best practices, and drive adoption across multiple teams.
Apply AI technologies and AI-assisted development to engineering and operational challenges, validating AI-generated code and using AI-assisted debugging and trace analysis.
Deploy, operate, and troubleshoot AI-powered services in production, including model API integrations, observability, latency, resource utilization, and operational costs.
Partner with applied scientists and software engineers to make AI-native delivery repeatable, measurable, and secure.