Senior Staff AI-Native DevOps Engineer

ServiceNow•Santa Clara, CA

About The Position

The Conversational AI team at ServiceNow builds the core platform behind ServiceNow Otto, the company’s 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 that connects the ServiceNow platform to services powering Otto. The role leads a new DevOps practice within the Conversational AI team and owns the build, deployment, and reliability of the systems behind ServiceNow Otto across SaaS environments, from commit through production. The 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, secure, and reliable. The role combines cloud infrastructure, containerization, orchestration, infrastructure as code, and practical understanding of how ML- and LLM-based services behave in production, with a focus on measurable improvements to deployment velocity and service reliability.

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.
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 deploying, scaling, and operating 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 with observability, monitoring, incident response, root cause analysis, and reliability remediation.
Experience architecting and optimizing cloud infrastructure across public and private cloud environments.
Strong problem-solving skills, including diagnosing distributed systems issues and improving performance, reliability, and operational efficiency.
Strong understanding of modern AI technologies, including LLMs, Generative AI, and agentic architectures, and the ability to apply them to engineering and operational challenges.
Practical experience using AI-assisted development tools in software engineering workflows, with the ability to validate, debug, and safely integrate AI-generated code.
Ability to critically review AI-generated code for logic errors, security anti-patterns, and missed edge cases.
Experience with AI-assisted debugging and trace analysis, and understanding of data privacy rules for information included in prompts.
Working knowledge of LLMs, Generative AI, and agentic AI concepts, including integration patterns and operational considerations.
Understanding of LLM-powered application architectures, including model inference, retrieval-augmented generation, 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 to manage application lifecycle and infrastructure operations.
Demonstrated technical leadership in 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 Infrastructure as Code and Kubernetes-native tooling.
Build and maintain CI/CD pipelines and GitOps workflows to automate testing, deployment, and release management.
Implement observability, monitoring, and incident response practices; perform root cause analysis and drive remediation.
Architect and optimize infrastructure across public and private cloud environments for 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 and improve operational efficiency; review and validate AI-generated code.
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.