Senior DevOps Engineer (Kubernetes) - Federal

ServiceNow•Santa Clara, CA

5h•$161,300 - $274,200

About The Position

The AI Services Platform Group at ServiceNow builds intelligent software and smart user experiences using advanced technologies. The group develops and evolves the AI platform while partnering with teams to create products and end-to-end AI-powered work experiences. This role contributes to AI and machine-learning solutions intended to improve enterprise service user experience and workflow efficiency, making those solutions accessible and scalable for users with varying levels of technical knowledge. The engineer builds cloud-based AI/ML solutions and collaborates with developers, product managers, and quality engineers. The role works with product owners to understand requirements and owns code through design, implementation, testing, and delivery. DevOps work includes deploying, scaling, and operating services in production Kubernetes environments; maintaining Helm-based deployments and CI/CD pipelines; owning observability; and independently resolving production incidents. The role participates in on-call, root-cause analysis, and operation against defined service level objectives. The position is listed as Regular, with a Flexible work persona. The stated base pay range is $161,300–$274,200, plus equity when applicable, variable/incentive compensation, and benefits. Benefits include health plans, flexible spending accounts, a 401(k) plan with company match, ESPP, matching donations, a flexible time away plan, and family leave programs.

Requirements

Expertise in Go or Python, design patterns, and time- and space-efficient algorithms.
Experience building new products that use challenging algorithms.
Expertise in writing efficient, object-oriented, modularized, high-quality software.
Knowledge of core AI/ML techniques and algorithms.
Knowledge of unit testing, profiling, and code tuning.
Experience deploying, scaling, and operating services in production Kubernetes environments.
Experience with Helm-based deployments and CI/CD pipelines.
Ability to own observability, including metrics, logs, tracing, and alerts.
Ability to debug and resolve production incidents independently.
Participation in on-call and root-cause analysis, and operation against defined service level objectives.

Nice To Haves

Confident use of AI coding assistants such as GitHub Copilot or Cursor, with rigorous review of generated code for logic errors, security anti-patterns, and edge cases.
AI-assisted debugging and trace analysis.
Understanding of data privacy rules governing information included in prompts.

Responsibilities

Build cloud-based AI/ML solutions for intelligent enterprise services.
Collaborate with developers, product managers, and quality engineers to produce quality software.
Work with product owners to understand detailed requirements.
Own code from design and implementation through testing and delivery.
Deploy, scale, and operate services in production Kubernetes environments.
Use Helm-based deployments and CI/CD pipelines to make releases repeatable and safely reversible.
Own observability for services, including metrics, logs, tracing, and customer-impact-focused alerts.
Independently debug and resolve production incidents.
Participate in on-call duties and root-cause analysis.
Operate against defined service level objectives.