Staff AI-Native DevOps Engineer

Join ServiceNow as a Staff AI-Native DevOps Engineer, where you will be responsible for the build, deployment, and reliability of our conversational AI platform, ServiceNow Otto. You will work closely with applied scientists and software engineers to ensure AI-native delivery is repeatable, measurable, and secure. Your expertise in cloud infrastructure, containerization, orchestration, and infrastructure as code will be essential in this role.

Posséder la responsabilité de la construction, du déploiement et de la fiabilité des systèmes derrière ServiceNow Otto, la plateforme d'IA conversationnelle.

USD 176,100 - 308,200

About ServiceNow

Collaborer étroitement avec des scientifiques appliqués et des ingénieurs logiciels pour rendre la livraison native à l'IA répétable, mesurable et sécurisée.

Responsibilities

• Déployer, mettre à l'échelle et exploiter des microservices distribués dans des environnements Kubernetes de production, y compris la gestion du cycle de vie des applications.
• Experience leading complex infrastructure initiatives, establishing engineering best practices, and driving adoption across multiple teams
• Deep expertise in designing, building, and operating scalable, reliable, and secure cloud-native applications and infrastructure
• 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
• 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
• 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
• Ability to deploy, operate, and troubleshoot AI-powered services in production, including managing model API integrations, observability, latency, resource utilization, and operational costs
• AI-assisted debugging and trace analysis, and a clear understanding of the data privacy rules governing what goes into a prompt

Requirements

• Strong problem-solving skills, with the ability to diagnose complex distributed systems issues and drive improvements in performance, reliability, and operational efficiency
• Strong software engineering skills, with experience developing infrastructure tooling and automation using Go, Python, or similar languages
• 4+ years of hands-on experience with Kubernetes, cloud infrastructure, and infrastructure automation
• Working knowledge of LLMs, Generative AI, and agentic AI concepts, including their integration patterns and operational considerations
• Practical experience using AI-assisted development tools in software engineering workflows, with demonstrated ability to validate, debug, and safely integrate AI-generated code
• 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
• 8+ years of professional software engineering, infrastructure engineering, DevOps, or related experience
• 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