AI Engineer - Voice & Agentic Systems
Posted Updated
This is a remote position.
Job Title: AI Engineer (Voice & Agentic Systems)
Location:Noida
Working Mode:Remote
Type:Full-time
Experience:2+ Years (shipping LLM agents)
About the Company:
weya AI builds AI automations - Voice, video and WhatsApp agents that banks, lenders and insurers put in front of their customers. Our agents handle collections, onboarding, renewals, KYC follow-ups and support calls at scale, across India, the Middle East & Africa, Australia and the US. We're a small team of 11-15 people. Every engineer ships to production, talks to customers, and owns real outcomes. If an agent mishandles a loan call, it costs a client money and trust, so we build for reliability first.
About the Role:
You'll design, build and run the agents that talk to our clients' customers: LLM orchestration with LlamaIndex/LangChain, FastAPI services behind them, and real-time voice pipelines wired into telephony providers. You own an agent end to end, from the first prompt to the 10,000th concurrent call.
What You'll Do?
- 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
What We're Looking For? (Non-Negotiable)
- 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.
Good to have (Bonus Points):
- 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
Get Real Experience on "What Success looks like?"
To apply:send your CV , GitHub or a demo of a voice agent you built, and one paragraph
on the hardest telephony problem you've solved. Open question: application email or link.