AI Agentic Engineer

360 Degree Cloud; Dadri; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

360 Degree Cloud is seeking an experienced AI Agent Engineer to design, develop, deploy, and operate production-grade Voice AI Agents. You will own the end-to-end pipeline from real-time speech processing to LLM-driven dialogue and TTS, ensuring low latency and robust telephony integration.

The role targets hands-on engineers who have built and delivered real voice agents at scale, with experience in VAD, streaming STT, and barge-in handling on self-hosted GPU infrastructure.

Qualifications

• Built a voice agent that handled real production calls at scale.
• Hands-on with VAD, streaming STT, LLM-based dialogue orchestration, streaming TTS, and barge-in handling.
• Experience with at least one voice orchestration framework.
• Experience serving LLM/STT/TTS models on GPU infrastructure.

Responsibilities

• Design and build the full voice agent pipeline: VAD, streaming STT, LLM-driven dialogue flow, streaming TTS, and barge-in/interruption handling.
• Own end-to-end latency — tune turn detection, streaming at every stage, GPU serving optimization; integrate with Telephony Infrastructure.
• Design and implement the agentic flow logic — routing, tool-calling, escalation/handoff logic, multi-step reasoning within the conversation.
• Deploy and serve models on self-hosted GPU infrastructure (vLLM, Triton, or equivalent) — not third‑party APIs.
• Instrument and own the numbers: WER, per-stage latency percentiles (p50/p95/p99), concurrency, conversation success/failure rate.

Skills

Production voice agent

End-to-end pipeline

Voice orchestration

GPU inference

Latency metrics

Tools

vLLM

TensorRT-LLM

Triton

LiveKit Agents

Pipecat

Job description

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 built a self-hosted, GPU‑served voice agent — real‑time STT → LLM (agentic flow) → TTS, with barge‑in, turn detection, and telephony integration over our existing infrastructure. This is not a "wire together some APIs" role. You'll 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've only built voice bots on top of a no‑code platform or a single hosted API (Vapi, Retell, Bland, etc.) without touching what's underneath, this role will be a stretch, not a fit.

What you'll 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 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've hit in production, concurrency they've 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).