Senior Staff AI-Native DevOps Engineer

About the team
The Conversational AI team at ServiceNow builds the core platform behind ServiceNow Otto, our conversational AI experience. We are a group of applied scientists and software engineers working at the intersection of research and production. Our work spans the full stack that makes Otto capable: agent orchestration, including harness engineering, prompt tuning, agent skills, policy design, and quality evaluation across languages; the A2A and MCP protocols that let agents collaborate and connect to tools; background channels and conversational triggers that bring agents into action; AI-native licensing and pricing; and the central caching layer that connects the ServiceNow platform to the services powering Otto.
We are seeking a Staff AI-Native DevOps Engineer to lead a new DevOps practice within the Conversational AI team. You will own the build, deployment, and reliability of the systems behind ServiceNow Otto, our conversational AI platform, from commit through production across our SaaS environments. Your work will span CI/CD pipelines, release automation, infrastructure on Kubernetes, observability, and the operational tooling that lets agent orchestration, MCP and A2A services, and the central caching layer ship safely and frequently. You will partner closely with applied scientists and software engineers to make AI-native delivery repeatable, measurable, and secure. This role combines deep expertise in cloud infrastructure, containerization, orchestration, and infrastructure as code with a practical understanding of how ML and LLM-based services behave in production, and a bias toward pragmatic engineering with measurable impact on deployment velocity and service reliability.
To be successful in this role you have:
Deep expertise in designing, building, and operating scalable, reliable, and secure cloud-native applications and infrastructure.
Strong software engineering skills, with 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, with the ability to diagnose complex distributed systems issues and drive improvements in performance, reliability, and operational efficiency.
Strong understanding of modern AI technologies, including Large Language Models (LLMs), Generative AI, and agentic architectures, with the ability to apply these concepts to engineering and operational challenges.
An AI-native engineering mindset, proactively leveraging AI tools and automation to accelerate development, improve operational efficiency, and deliver more intelligent, resilient systems.
DevOps:
Deploy, scale, and operate distributed microservices in production Kubernetes environments, including managing application lifecycle, networking, security, and service reliability.
Design and implement infrastructure automation using Infrastructure as Code (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 to automate testing, deployment, and release management.
Implement observability, monitoring, and incident response practices, performing root cause analysis and driving remediation to improve availability, fault tolerance, and operational resilience.
Architect and optimize cloud infrastructure across public and private cloud environments, with an emphasis on security, scalability, cost efficiency, and operational simplicity.
AI skills:
Artificial intelligence strategy is a critical skill at your level — it applies to how you build and to what you build.
Confident use of AI coding assistants such as GitHub Copilot or Cursor, paired with the critical eye to review AI-generated code as rigorously as any other — catching logic errors, security anti-patterns, and missed edge cases rather than treating model output as production-ready.
AI-assisted debugging and trace analysis, and a clear understanding of the data privacy rules governing what goes into a prompt.
Understanding of LLM-powered application architectures, including model inference, retrieval-augmented generation (RAG), tool calling, and agentic workflows, with an awareness of their infrastructure, scalability, and reliability implications.
Ability to deploy, operate, and troubleshoot AI-powered services in production, including managing model API integrations, observability, latency, resource utilization, and operational costs.
Minimum 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, with 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 their integration patterns and operational considerations.
Preferred requirements
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 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.
For positions in this location, we offer a base pay of $201,300 - $352,300, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location.

Employment type
Full-time

Work arrangement
Hybrid