Senior Staff AI-Native DevOps Engineer

ServiceNow's Conversational AI team as a Senior Staff AI-Native DevOps Engineer. In this role, you will lead a new DevOps practice, owning the build, deployment, and reliability of the systems behind ServiceNow Otto, our conversational AI platform. You will work closely with applied scientists and software engineers to make AI-native delivery repeatable, measurable, and secure. This position requires deep expertise in cloud infrastructure, containerization, orchestration, and infrastructure as code, as well as a practical understanding of how ML and LLM-based services behave in production.

Lead the development and implementation of DevOps practices within the Conversational AI team, focusing on CI/CD pipelines, release automation, and infrastructure on Kubernetes.

Collaborate closely with applied scientists and software engineers to ensure the reliability and scalability of the systems behind ServiceNow Otto, from commit through production.

Drive the adoption of engineering best practices across multiple teams, establishing standards for infrastructure automation, observability, and incident response.

Responsibilities

• 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
• 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
• AI-assisted debugging and trace analysis, and a clear understanding of the data privacy rules governing what goes into a prompt
• Ability to deploy, operate, and troubleshoot AI-powered services in production, including managing model API integrations, observability, latency, resource utilization, and operational costs

Requirements

• Artificial intelligence strategy is a critical skill at your level — it applies to how you build and to what you build
• 6+ years of experience deploying and operating containerized applications and distributed services in production environments
• 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
• 4+ years of hands-on experience with Kubernetes, cloud infrastructure, and infrastructure automation
• 8+ years of professional software engineering, infrastructure engineering, DevOps, or related experience
• 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

USD 201,300 - 352,300