AI Engineer - Voice & Agentic Systems

This is a remote position.

• Build agentic workflows with LlamaIndex and LangChain: tool calling, RAG over policy and loan documents, multi-step reasoning, and guardrails for regulated conversations.
  
• Design and run FastAPI services that serve agents in real time: async endpoints, WebSockets, webhooks, background tasks and clean API contracts.
  
• Build low-latency voice agents end to end: STT, LLM, TTS, voice activity detection, barge-in/interruption handling, and turn-taking.
  
• Integrate and maintain telephony providers (e.g. Twilio, Plivo, Exotel, Vonage, SIP trunks): inbound/outbound calls, call transfer, recordings, DTMF, and media streaming.
  
• Engineer for failure: provider outages, dropped calls, LLM timeouts, partial transcripts, retries and idempotency, so one bad dependency never takes down a campaign.
  
• Write tests that catch problems before clients do: unit and integration tests, simulated calls, load tests, and regression suites for agent behaviour.
  
• Instrument everything: latency per turn, call success rates, cost per call, and alerts that fire before a client notices.
  
• Work directly with FDEs and clients to turn real call transcripts into better agents.
  

Requirements

• Agentic frameworks: shipped production agents with LlamaIndex and/or LangChain (or LangGraph), including tool use, memory and RAG. Not just notebooks.
  
• FastAPI: extensive experience building async Python services with FastAPI, Pydantic, WebSockets and background workers, under real traffic.
  
• Voice agents: built at least one voice agent that handled real customer calls, with hands-on work on STT/TTS providers (e.g. Deepgram, ElevenLabs, Azure, Google) and latency tuning.
  
• Telephony: integrated two or more telephony providers or SIP setups, and can explain how audio flows from a phone network to your code and back.
  
• Production mindset: you think about concurrency, rate limits, timeouts, retries and edge cases before writing the happy path, and you write tests for them.
  
• Debugging under pressure: comfortable reading logs, call recordings and traces to find why a call failed.
  
• Clear written English; able to explain a technical trade-off to a client.
  
  

• Real-time voice frameworks such as LiveKit Agents or Vapi, or speech-to-speech models.
  
• Go for high-throughput services alongside Python.
  
• Multilingual voice work, especially Hindi, Hinglish, Arabic or other Indic languages.
  
• Experience in BFSI or other regulated domains: consent, call recording rules, PII handling, audit trails.
  
• LLM evaluation: automated scoring of agent conversations, prompt regression testing.
  
• WhatsApp Business API or video agent experience.
  
• Docker, Kubernetes, AWS/GCP, Redis, Postgres, and queues like Kafka or RabbitMQ.
  

Benefits