Senior Staff AI-Native DevOps Engineer

ServiceNow•Santa Clara, CA

2d•$181,200 - $317,100

About The Position

The Conversational AI team at ServiceNow builds the core platform behind ServiceNow Otto, a conversational AI experience. The team works across agent orchestration, A2A and MCP protocols, background channels and conversational triggers, AI-native licensing and pricing, and the central caching layer connecting the ServiceNow platform to Otto services. The Senior Staff AI-Native DevOps Engineer will lead a new DevOps practice within the Conversational AI team and own the build, deployment, and reliability of the systems behind ServiceNow Otto across SaaS environments, from commit through production. The role spans 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. The engineer will partner with applied scientists and software engineers to make AI-native delivery repeatable, measurable, and secure. The position combines cloud infrastructure, containerization, orchestration, and infrastructure-as-code expertise with a practical understanding of how ML- and LLM-based services operate in production. It calls for pragmatic engineering focused on measurable improvements in deployment velocity and service reliability. Responsibilities include operating production Kubernetes microservices; designing IaC and Kubernetes-native automation; building CI/CD and GitOps workflows; implementing observability and incident response; and architecting secure, scalable, cost-efficient cloud infrastructure. The engineer will also apply AI tools and operational knowledge of LLM, generative AI, retrieval-augmented generation, tool calling, and agentic workflows to engineering challenges.

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, and experience building production-grade automation and tooling.
Practical experience using AI-assisted development tools in software engineering workflows, with the 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.
Strong software engineering skills, including experience developing infrastructure tooling and automation using Go, Python, or similar languages.
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 deploy, scale, and operate distributed microservices in production Kubernetes environments.
Experience with infrastructure-as-code and Kubernetes-native tooling, including Terraform, Ansible, Helm, and Kubernetes Operators.
Experience with CI/CD pipelines and GitOps workflows, using tools such as Jenkins, GitHub Actions, Tekton, and ArgoCD.
Experience implementing observability, monitoring, incident response, root cause analysis, and remediation.
Ability to architect and optimize public and private cloud infrastructure for security, scalability, cost efficiency, and operational simplicity.
Confident use of AI coding assistants such as GitHub Copilot or Cursor, with the ability to rigorously review AI-generated code for logic errors, security anti-patterns, and edge cases.
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.

Nice To Haves

At least 12 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 for 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

Lead a new DevOps practice within the Conversational AI team.
Own the build, deployment, and reliability of ServiceNow Otto systems 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 IaC 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 infrastructure across public and private cloud environments, emphasizing security, scalability, cost efficiency, and operational simplicity.
Partner with applied scientists and software engineers to make AI-native delivery repeatable, measurable, and secure.
Use AI tools and automation to accelerate development, improve operational efficiency, and deliver more intelligent, resilient systems.
Apply AI-assisted debugging and trace analysis while respecting data privacy rules for prompt content.
Deploy, operate, and troubleshoot AI-powered services in production, including model API integrations, observability, latency, resource utilization, and operational costs.
Lead complex infrastructure initiatives, establish engineering best practices, and drive adoption across multiple teams.