AI Agentic Developer (Voice AI & Autonomous Sales CRM)

USG, Inc.

55m ago • Remote

Senior

Remote

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Quick Facts

  • Role: AI Agentic Developer (Voice AI & Autonomous Sales CRM)

  • Work style: Remote (USA)

  • Employment: Contract, immediate start

Description

Join a hands-on engineering effort to build real-time, voice-enabled autonomous AI agents for next-generation Sales CRM workflows. You’ll combine low-latency voice streaming, stateful multi-agent orchestration, and production-grade backend engineering so agents can hold natural conversations and take actions inside a CRM.

Responsibilities

  • Build conversational voice agents using LiveKit Agents, Pipecat, or similar real-time frameworks

  • Design and tune STT/TTS pipelines for low latency and natural-sounding conversation

  • Engineer real-time audio streaming over WebSockets/WebRTC, including VAD, dynamic interruption handling, and turn-taking logic

  • Build stateful, multi-agent workflows with tool calling using LangGraph and LangChain

  • Integrate multiple LLM providers (OpenAI, Anthropic Claude, Google Gemini)

  • Use graph-based architectures, graph databases, and GraphRAG for knowledge retrieval and contextual grounding

  • Build and integrate REST APIs, and ship containerized services with tracing and evaluation in place

Requirements

  • Strong Python with robust asynchronous programming (asyncio) and scalable backend design

  • Hands-on experience with LiveKit Agents, Pipecat, or equivalent voice agent frameworks

  • STT experience (Deepgram, Whisper, AssemblyAI, or equivalent)

  • TTS experience (ElevenLabs, OpenAI TTS, Azure Speech, or equivalent)

  • Real-time audio streaming experience (WebSockets/WebRTC) with low-latency pipelines, VAD, and interruption/turn-taking logic

  • LangGraph and LangChain experience for stateful orchestration and tool calling

  • Multi-provider LLM integration experience (OpenAI, Claude, Gemini)

  • Graph databases and GraphRAG experience

  • Observability and tracing experience (OpenTelemetry, LangSmith) and LLM evaluation frameworks

  • Docker/containerized deployment experience

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