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 join 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.

Qualifications :

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

• 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 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 $176100 - $308200 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.

Additional Information :

Work Personas

We approach our distributed world of work with flexibility and trust. Work personas (flexible remote or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service.

Equal Opportunity Employer

ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race color religion sex sexual orientation national origin age disability gender identity  veteran status or any other category protected by addition all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. 

Accommodations

We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process or are unable to use this online application and need an alternative method to apply please contact for assistance. 

Export Control Regulations

For positions requiring access to controlled technology subject to export control regulations including the U.S. Export Administration Regulations (EAR) ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. 

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Remote Work :

No

Employment Type :

Full-time