NoidaOn-siteDirect career pageEst. 20–45 LPA
Posted on 1 Sept
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Reach people at 360 Degree Cloud (360 Degree Cloud Technologies Pvt. Ltd.)
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at 360 Degree Cloud (360 Degree Cloud Technologies Pvt. Ltd.) you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, build, and own the end-to-end production-grade Voice AI agent pipeline, including VAD, streaming STT, LLM dialogue flow, streaming TTS, barge-in/interruption handling, and telephony integration.
- Manage end-to-end latency budgets and GPU serving optimization (batching, quantization, concurrency) for real-time voice agents.
- Implement and operate agentic logic for conversation management including routing, tool-calling, escalation, multi-step reasoning, and performance metrics monitoring (WER, latency percentiles, concurrency, success/failure rate).
Minimum Requirements
- Proven experience building and operating a production voice agent handling real user/customer calls at scale, beyond demos or prototypes.
- Hands-on expertise with the full stack: VAD/turn detection, streaming STT, LLM-based dialogue orchestration, streaming TTS, barge-in handling.
- Experience serving LLM/STT/TTS models on GPU infrastructure using frameworks like vLLM, Triton; no exclusive use of hosted model APIs.
- Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Strong expertise with voice orchestration frameworks (e.g., Pipecat, LiveKit Agents) and practical problem-solving on real limitations encountered in production.
- Deep understanding of latency, concurrency, and scalability tradeoffs in AI voice agent pipelines with demonstrated incident management experience.
- Experience with GPU-based model serving infrastructure and managing complex agentic dialogue flows optimizing real-time performance.