AI Prompt Engineer - Voice

We are hiring a Voice AI Prompt Engineer to own prompt design and agentic workflow engineering across our Voice AI platform. You will build prompts for real-time telephony systems, IVR and outbound bots, multilingual conversational agents, and post-call analytics as well as multi-step agentic LLM applications. You understand ASR noise, spoken language constraints, and latency budgets, and you work systematically to close the gap between what a model can do and what a voice product needs it to do.

WHAT YOU WILL DO:

Voice Agent Prompting: Design ASR-noise-robust prompts for IVR bots, outbound calling agents, intent detection, entity extraction, and dialog state tracking within real-time latency budgets

Spoken Output Design: Optimize LLM responses for TTS delivery concise, natural-sounding, and telephony-appropriate (no markdown, prosody-aware)

Multilingual Handling: Engineer prompt strategies for code-mixed input (Hinglish, Tanglish, and other Indian language mixes) with robust fallback logic

Agentic Pipelines: Build ReAct/chain-of-thought workflows, tool-calling pipelines, and RAG-backed agents for voice and non-voice enterprise applications

Evaluation & Testing: Build prompt evaluation frameworks with voice-specific metrics; maintain a versioned prompt library with regression tests

Context Management: Handle long multi-turn voice conversations summarization, memory injection, and context prioritization strategies

LLM Integration: Embed prompt logic into production inference pipelines; design structured output schemas for downstream telephony integrations

Cross-Team Collaboration: Translate product requirements into prompt specifications; curate instruction datasets for fine-tuning from prompt experiments

MUST HAVES:

- Experience 36 yrs with LLMs

- 1+ year in voice AI or conversational AI Hands-on experience with ASR output characteristics and downstream LLM effects.

- Experience with Agentintic frameworks like LangChain, LlamaIndex, LangGraph, or equivalent RAG architectures like chunking, retrieval, prompt integration.

- Strong Python experience.

- Experience on evaluation pipelines, API integration, data processing Prompt evaluation with quantitative metrics and regression testing.

- Strong critical reading of model outputs with precise failure mode articulation.