Staff AI-Native DevOps Engineer

ServiceNow•Santa Clara, CA

6h•$176,100 - $308,200

About The Position

The Staff AI-Native DevOps Engineer will join ServiceNow’s Conversational AI team, which builds the core platform behind ServiceNow Otto. The role owns the build, deployment, and reliability of the systems behind the conversational AI platform, from commit through production across SaaS environments. It covers 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 will partner with applied scientists and software engineers to make AI-native delivery repeatable, measurable, and secure. The position combines cloud infrastructure, containerization, orchestration, and infrastructure-as-code expertise with practical understanding of production ML and LLM services, with emphasis on deployment velocity and service reliability. Responsibilities include deploying and operating distributed microservices in Kubernetes; automating infrastructure and delivery; improving observability and incident response; and architecting cloud infrastructure across public and private cloud environments. The role also applies AI tools and knowledge of LLM-powered application architectures to production engineering, including AI service integrations, operations, and troubleshooting.

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 software engineering skills, including experience developing infrastructure tooling and automation using Go, Python, or similar languages.
Strong problem-solving skills, including diagnosing complex distributed systems issues and improving performance, reliability, and operational efficiency.
Strong understanding of LLMs, Generative AI, and agentic architectures, and ability to apply these concepts to engineering and operational challenges.
Practical experience using AI-assisted development tools in software engineering workflows, with 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.
Understanding of LLM-powered application architectures, including model inference, retrieval-augmented generation (RAG), tool calling, and agentic workflows, and awareness of infrastructure, scalability, and reliability implications.
Confident use of AI coding assistants such as GitHub Copilot or Cursor, with the ability to critically review generated code.
AI-assisted debugging and trace analysis skills, and understanding of data privacy rules governing prompt content.

Nice To Haves

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.
8+ years of experience in software engineering, infrastructure engineering, DevOps, or platform engineering.

Responsibilities

Own the build, deployment, and reliability of the systems behind ServiceNow Otto across SaaS environments, from commit through production.
Design, build, and operate scalable, reliable, and secure cloud-native applications and infrastructure.
Deploy, scale, and operate distributed microservices in production Kubernetes environments, including 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 to automate testing, deployment, and release management.
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.
Lead complex infrastructure initiatives, establish engineering best practices, and drive their adoption across teams.
Use AI tools and automation to accelerate development, improve operational efficiency, and deliver more intelligent, resilient systems.
Review, validate, and debug AI-generated code, including checking for logic errors, security anti-patterns, and missed edge cases; follow data privacy rules for prompt inputs.
Deploy, operate, and troubleshoot AI-powered services in production, including model API integrations, observability, latency, resource utilization, and operational costs.
Partner with applied scientists and software engineers to make AI-native delivery repeatable, measurable, and secure.