Voice AI Engineer

Role Overview

Avashya is an AI-first, platform-led services company founded by engineers and specialists with experience at AWS and Microsoft. This Voice AI Engineer role owns the real-time voice platform and every client implementation built on it—closing the gap between a demo and a production-grade voice AI product.

Responsibilities

• Extend the real-time voice framework; when configuration reaches its limits, work directly with the runtime.

• Turn lessons from each client build into reusable platform capabilities for the next.

• Take client use cases—such as outbound negotiation, multilingual support, and appointment booking—from scoping through go-live.

• Tune ASR, LLM, TTS, RAG, tool-calling, and orchestration to the client, languages, and call volume rather than relying on a default stack.

• Remain the technical owner after launch and diagnose production behaviour quickly.

• Set latency budgets for every stage of a conversation and maintain them as load and providers change.

• Gate releases on measured evaluations; investigate degraded call quality before clients need to report it.

• Make client-specific tradeoffs between speed and polish; review code and mentor as the team grows.

Required Depth

Architecture and real-time voice systems:

• Deep, hands-on knowledge of Pipecat, LiveKit Agents, or comparable orchestration frameworks, including their internals and limits.

• Ability to select cascaded ASR/LLM/TTS pipelines or speech-to-speech approaches based on control, swapability, latency, and prosody needs.

• Strong turn-taking expertise: VAD tuning, endpointing, barge-in, and natural handling of interruptions.

• WebRTC and SIP telephony experience, including jitter buffers, PSTN versus browser tradeoffs, and Opus, PCM, and mu-law codecs.

Latency, retrieval, and tool use:

• Ability to decompose end-to-end latency across ASR partials, LLM time-to-first-token, TTS time-to-first-audio-byte, network hops, and tool-calling round trips.

• Experience streaming ASR, LLM, and TTS together, using approaches such as fillers, early commits, or speculative synthesis when tool calls are slow.

• Ability to use RAG and live tool calls while preserving a natural, live conversation.

Evaluation and guardrails:

• Voice-specific evaluation of ASR accuracy (WER), TTS naturalness, and end-to-end conversation quality.

• Track P50, P95, and P99 latency by stage and provider over time.

• Implement content safety, hallucination control, PII handling for transcripts and recordings, consent, and data-residency practices for recorded calls.

Experience

Depth matters more than years of experience. This role calls for senior-level ownership of production voice AI systems.

Compensation

Top-of-market; pay reflects depth, not tenure.