Staff SRE Engineer - Voice Core Platform

It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better. We're building an AI-native culture where technology and talent are unstoppable together. And we're just getting started.

Join us to put AI to work for people.

About the role

We are building a real-time conversational AI voice platform. An orchestrator runs every call through Voice Activity Detection, Speech-to-Text, LLM-based agent matching, tool calling against ServiceNow platform (Glide) APIs, and Text-to-Speech.

In voice, latency and reliability are the product. You will own the platform's reliability, performance and operational architecture, and set direction across teams.

Responsibilities

Define and drive SLIs/SLOs for availability, perceived latency (end-of-speech to first audio), and call quality.; Instrument end-to-end tracing across every pipeline stage, and lead architectural work to cut latency.; Design autoscaling and capacity plans for stateful, real-time connections (WebSocket, WebRTC, SIP/RTP).; Lead load, soak and breakpoint testing, and turn results into capacity models.; Build resilience for LLM, STT and TTS dependencies: failover, fallbacks, rate-limit handling and graceful degradation.; Own Kubernetes architecture, infrastructure-as-code and CI/CD with safe progressive delivery.; Lead incident response and postmortems, and mentor engineers across the team.

Requirements

Experience in leveraging or critically thinking about how to integrate AI into 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.; 8+ years in SRE, DevOps, production engineering or software engineering on production systems at scale.; 1+ year building or operating real-time voice systems (VoIP, SIP/RTP, WebRTC, or STT/TTS integration).; 2+ years building or operating AI agentic systems and orchestration pipelines (agent routing, tool calling, multi-step LLM workflows).; 1+ year deploying and operating LLMs in production (model serving, inference optimization, provider integration and failover).; Strong expertise in Kubernetes, cloud infrastructure ([AWS / Azure / GCP]) and Terraform or similar.; Hands-on observability experience (OpenTelemetry, Prometheus, Grafana, Datadog or similar).; Proficiency in Python, Go or Java.

Nice to have

Experience Inference serving experience (vLLM, Triton, GPU scheduling).; Familiarity with the ServiceNow platform or enterprise SaaS backends.