AI Agentic Engineer

We are looking for a highly experienced AI Agent Engineer who has hands-on experience in designing, developing, deploying, and operating production-grade Voice AI Agents.

This role is specifically for candidates who have already built and successfully delivered AI Voice Agents and have worked on real-world voice automation systems, not candidates who have only experimented with ChatGPT, LLM APIs, basic voice bots, or demo-level implementations.

The ideal candidate should have built a self-hosted, GPU-served voice agent: real-time STT to LLM (agentic flow) to TTS, with barge-in, turn detection, and telephony integration over our existing infrastructure. This is not a wire together some APIs role. You will own the pipeline end to end: model serving, latency budgets, interruption handling, and the agentic logic that decides what the agent does mid-conversation, not just what it says.

If you have only built voice bots on top of a no-code platform or a single hosted API (Vapi, Retell, Bland, etc.) without touching what is underneath, this role will be a stretch, not a fit.

What you will do

• Design and build the full voice agent pipeline: VAD, streaming STT, LLM-driven agentic dialogue flow, streaming TTS, and barge-in/interruption handling.
• Own end-to-end latency: turn detection tuning, streaming at every stage, GPU serving optimization (batching, quantization, concurrency). Integrate the pipeline with Telephony Infrastructure.
• Design and implement the agentic flow logic: routing, tool-calling, escalation/handoff logic, multi-step reasoning within the conversation, and know when not to chain multiple agent calls inside a live turn because of latency cost.
• Deploy and serve models on self-hosted GPU infrastructure (vLLM, Triton, or equivalent), not calling third-party inference APIs.
• Instrument and own the numbers: WER, per-stage latency percentiles (p50/p95/p99), concurrency handled, conversation success/failure rate.

Must-have (non-negotiable)

• Has personally built a voice agent that handled real user or customer calls in production, at real volume, not a hackathon demo, not a course project, not a prototype that never left staging.
• Hands-on experience with the full pipeline stack: VAD/turn detection, streaming STT, LLM-based dialogue orchestration, streaming TTS, and barge-in handling, and can speak to tradeoffs in each, not just name-drop the tools.
• Direct experience with at least one voice orchestration framework (Pipecat, LiveKit Agents, or equivalent), and can describe a real limitation they hit with it, not just what the docs say it does.
• Experience serving LLM/STT/TTS models on GPU infrastructure directly (vLLM, TensorRT-LLM, Triton, or similar), not exclusively consuming hosted model APIs.
• Can talk numbers: actual latency percentiles they have hit in production, concurrency they have scaled to, and at least one real incident, what broke, why, and how they fixed it.

Nice-to-have

• Multilingual/Indic language voice AI experience (Hindi or other Indian languages, code-switching).
• Experience with real-time noise suppression / echo cancellation (RNNoise, DeepFilterNet, or similar).
• Agentic frameworks beyond the voice stack (LangGraph, CrewAI, or comparable) for the reasoning/flow layer.
• Experience with semantic turn detection (beyond fixed-silence VAD endpointing).