Staff Voice AI Engineer – SRE/DevOps

ServiceNow•Santa Clara, CA

1d•$176,100 - $308,200•Hybrid

About The Position

As a Staff Voice AI Engineer – SRE/DevOps, you will build software solutions to solve operational problems in the cloud-native space for Voice AI, including deployments, observability, monitoring, high availability, disaster recovery, and continuous integration. You will build high-quality, clean, scalable, and reusable code by enforcing best practices around software engineering architecture and processes such as code reviews and unit testing. You will work with product owners to understand detailed requirements and own your code from design, implementation, test automation, and delivery. Experience integrating LLMs into voice platforms and real-time communication systems is essential. You will also serve as a mentor for colleagues and help promote knowledge-sharing across telecom and AI engineering disciplines.

Requirements

4+ years of experience operating highly available distributed cloud-native workloads running on container platforms (e.g. Kubernetes)
4+ years of development experience with Python, GoLang, Java or similar languages.
Experience integrating applications on top of LLMs (Prompt Engineering Agents and Skills);
Working experience building distributed systems with cloud-native software;
Experience with software-defined networking, infrastructure as code and configuration management;
Critically thinking around AI integrations for work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry.
Experience building with AI tools such as Claude Code, Cursor, Windsurf.

Nice To Haves

2+ years of experience building and operating Voice AI applications is a strong asset
Experience with DevOps tooling (e.g. Helm / Ansible / Kubernetes / Prometheus /Splunk/ GitLab CI) is considered an asset

Responsibilities

Build software solutions to solve operational problems in the cloud-native space for Voice AI (deployments, observability, monitoring, high availability, disaster recovery, continuous integration, ...)
Build high-quality, clean, scalable and reusable code by enforcing best practices around software engineering architecture and processes (Code Reviews, Unit testing, etc.).
Work with product owners to understand detailed requirements and own your code from design, implementation, test automation, and delivery
Experience integrating LLMs into voice platforms and real-time communication systems.
Be a mentor for colleagues and help promote knowledge-sharing across telecom and AI engineering disciplines.