Staff AI-Native DevOps Engineer

ServiceNow•San Diego, CA

2d•$158,500 - $277,400

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 Otto’s systems from commit through production across SaaS environments. It spans CI/CD pipelines, release automation, Kubernetes infrastructure, observability, and operational tooling supporting agent orchestration, MCP and A2A services, and the central caching layer. 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, infrastructure as code, and practical understanding of ML- and LLM-based services in production, with a focus on improving deployment velocity and service reliability. The Conversational AI team works across agent orchestration, harness engineering, prompt tuning, agent skills, policy design, multilingual quality evaluation, A2A and MCP protocols, background channels and conversational triggers, AI-native licensing and pricing, and the caching layer connecting the ServiceNow platform to services powering Otto.

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, 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 problem-solving skills, including diagnosing complex distributed-systems issues and improving performance, reliability, and operational efficiency.
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.
Strong understanding of modern AI technologies and their application to engineering and operational challenges.
Understanding of LLM-powered application architectures, including model inference, retrieval-augmented generation, tool calling, and agentic workflows, and their infrastructure, scalability, and reliability implications.
Ability to critically review AI-generated code for logic errors, security anti-patterns, and missed edge cases.
Understanding of data privacy rules governing information included in prompts.
Experience with Kubernetes, Infrastructure as Code, and Kubernetes-native tooling, including Terraform, Ansible, Helm, and Kubernetes Operators.
Experience with CI/CD and GitOps tools such as Jenkins, GitHub Actions, Tekton, and ArgoCD.

Nice To Haves

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 for application lifecycle and infrastructure operations.
Demonstrated technical leadership in cross-team infrastructure initiatives, production reliability improvements, and scalable 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 to improve platform reliability and engineering productivity.

Responsibilities

Own the build, deployment, and reliability of 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.
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 cloud infrastructure across public and private cloud environments for security, scalability, cost efficiency, and operational simplicity.
Develop infrastructure tooling and automation using Go, Python, or similar languages.
Partner with applied scientists and software engineers to make AI-native delivery repeatable, measurable, and secure.
Apply AI-assisted development, debugging, and trace analysis while reviewing generated code and following data privacy rules.
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 teams.