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Voice AI Prompt Engineer
Full-time
About the Company
Gnani.ai is a conversational AI company building voice-first, agentic AI platforms that automate customer interactions across voice, chat, and workflows in more than 40 languages, including 12+ Indian languages. The company serves over 200 global enterprises, including Fortune 500 brands, and processes more than 30 million voice interactions every day. Its solutions are used across BFSI, retail, automotive, healthcare, telecom, government, and other enterprise environments where scale, accuracy, and multilingual performance matter.
About the Role
This role owns prompt design and agentic workflow engineering for a live Voice AI platform. The work spans real-time telephony systems, IVR and outbound bots, multilingual conversational agents, post-call analytics, and multi-step LLM applications. Success depends on understanding ASR noise, spoken-language constraints, and latency budgets, then turning those constraints into prompts and workflows that perform reliably in production. The right candidate will treat prompts as production assets, not one-off experiments, and will know how to improve call containment, conversation quality, and downstream enterprise outcomes through disciplined iteration.
Key Responsibilities
Design ASR-noise-robust prompts for IVR bots, outbound calling agents, intent detection, entity extraction, and dialog state tracking.
Optimize spoken responses for TTS delivery so they sound concise, natural, and telephony-appropriate.
Engineer prompt strategies for code-mixed and multilingual inputs such as Hinglish and Tanglish.
Build agentic workflows using ReAct-style reasoning, tool calling, and RAG-backed patterns.
Create prompt evaluation frameworks with voice-specific metrics, regression tests, and versioned prompt libraries.
Manage long, multi-turn conversations through summarization, memory injection, and context prioritization.
Embed prompt logic into production inference pipelines and define structured output schemas.
Translate product requirements into prompt specifications and curated instruction datasets.
Essential Skills & Technologies
Strong Python experience with the ability to build prompt tooling, evaluation scripts, API integrations, and data-processing flows.
Hands-on experience with LLMs and conversational AI, including practical understanding of how model behavior changes when ASR outputs, latency, and spoken-language constraints are introduced.
Experience with agentic frameworks such as LangChain, LlamaIndex, LangGraph, or equivalent RAG architectures.
Ability to build evaluation pipelines with quantitative metrics, regression testing, and failure-mode analysis.
Practical understanding of voice AI systems, including telephony integrations, IVR flows, outbound bot behavior.
Familiarity with structured outputs, production inference pipelines, and API-based integration patterns.
Additional Plus
Experience with multilingual Indian-language voice systems and code-mixed speech handling in live production environments.
Exposure to fine-tuning data curation, prompt experiment analysis, or post-call analytics workflows in enterprise AI settings.
Experience working on systems where call containment, customer experience, and operational efficiency are measured as direct business outcomes.
What You'll Bring
3-6 years of experience working with LLMs.
At least 1 year of hands-on experience in voice AI or conversational AI.
Strong Python skills, disciplined experimentation habits.
Experience with agentic and retrieval-based systems.
