Staff SRE Engineer - Voice Core Platform

Job highlights

• Experience in leveraging or critically thinking about how to integrate AI into work processes,decision-making,or problem-solving
• Experience Inference serving experience (vLLM,Triton,GPU scheduling).

Job Description

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 platforms reliability, performance and operational architecture, and set direction across teams.

What youll do

• 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. Qualifications

To be successful in this role you have:

• 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.

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