We are hiring an AI Engineer (Prompt Engineering) 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 DsPy/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

Experience 3 - 6 years with LLMs :

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

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

- Strong Python experience.

- Experience with Pipecat, Livekit are a major plus.

- 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