Senior Staff AI-Native DevOps Engineer

ServiceNow•San Diego, CA

2d•$181,200 - $317,100

About The Position

The Senior Staff AI-Native DevOps Engineer will lead a new DevOps practice within ServiceNow’s Conversational AI team, owning the build, deployment, and reliability of the systems behind ServiceNow Otto across SaaS environments, from commit through production. The role covers 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 expertise in cloud infrastructure, containerization, orchestration, and infrastructure as code with practical understanding of production ML and LLM services, prioritizing measurable improvements in deployment velocity and service reliability. The Conversational AI team develops the core platform behind ServiceNow Otto. Its work includes 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 central caching layer connecting the ServiceNow platform to Otto services. The role requires leading complex infrastructure initiatives and establishing engineering practices across teams. The engineer will deploy and operate distributed microservices in Kubernetes, automate infrastructure and delivery, build observability and incident response practices, and architect cloud infrastructure across public and private environments. They will also apply AI tools responsibly, understand LLM-powered application architectures and their operational implications, and deploy, operate, and troubleshoot AI-powered services in production. The stated base pay for positions in the applicable location is $181,200–$317,100, plus equity when applicable, variable/incentive compensation, and benefits. Individual total compensation varies based on qualifications, skills, competencies, and work location. The position is listed as Regular, with a Flexible work persona.

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, with experience building production-grade automation and tooling.
Practical experience using AI-assisted development tools in software engineering workflows, including validating, debugging, and safely integrating 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 and 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.
Confident use of AI coding assistants such as GitHub Copilot or Cursor, with ability to critically review AI-generated code for logic errors, security anti-patterns, and missed 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 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.
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.
Develop and maintain CI/CD pipelines, release automation, infrastructure on Kubernetes, observability, and operational tooling.
Enable safe and frequent delivery of agent orchestration, MCP and A2A services, and the central caching layer.
Partner with applied scientists and software engineers to make AI-native delivery repeatable, measurable, and secure.
Deploy, scale, and operate distributed microservices in production Kubernetes environments, managing 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 public and private cloud infrastructure for security, scalability, cost efficiency, and operational simplicity.
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.