Voice AI & Infrastructure Engineer

Role overview Own a production voice-agent product from conversation design through the services and infrastructure that run it. The work focuses on making real-time conversations natural and dependable, then scaling the system for high call volumes with low latency.

Responsibilities - Improve conversation flows, turn-taking, and interruption handling across voice agents. - Diagnose issues such as mispronunciations, generated speech artifacts, repeated loops, and inconsistent execution; add monitoring to catch regressions. - Design prompts and flows for real-time language and voice APIs, choosing between prompt changes and architectural fixes. - Tune the speech pipeline from voice activity detection and transcription through language generation and speech output against a sub-500ms latency target. - Build and operate telephony, inbound and outbound call handling, and multi-tenant cloud services, including deployment and scaling. - Provide call analytics, latency traces, and audit trails to make production behavior easier to investigate.

Requirements - 2–4 years of experience and evidence of shipping at least one real-time voice agent into production. - Strong Python skills and ability to own backend services end to end. - Hands-on experience with a real-time voice or language API, or a speech-to-text, language-model, and text-to-speech pipeline; practical prompt and flow design skills. - Experience deploying and operating services on AWS, including containers and queues. - Strong debugging skills for non-deterministic systems, including tracing the causes of loops or hallucinations.

Nice to have - WebRTC, SIP, telephony, LiveKit, or Pipecat experience. - MongoDB, event pipelines, analytics, or multi-tenant SaaS scaling experience. - Hinglish or multilingual conversation design; browser automation or scraping experience.