Headline

Sr Staff AI Engineer | Multimodal Foundational AI | ASR · LLM · TTS · Conversational AI | Indic Language AI | 7+ yrs Building Real-World NLP at Scale

About

What I Do I build production-grade NLP and voice AI systems — from multimodal Audio LLMs to low-latency speech pipelines. With 6+ years at gnani.ai, I've worked across the full stack of conversational AI: from training TTS models from scratch to deploying RAG-based knowledge systems serving thousands of enterprise users. Recent Highlight: Contributed to gnani.ai's Multimodal Speech Audio LLM — a model that fuses acoustic signals with language understanding to enable context-aware, real-time voice intelligence. This sits at the frontier of where speech processing meets large language models. Impact I've Delivered: - Optimized voice bot latency to under 2 seconds for Indic languages via enhanced orchestration & inference - Achieved 35% improvement in suggestion accuracy with DPO fine-tuning + 3x cost savings vs. OpenAI - Built RAG knowledge system that cut bot development time by 20% - Scaled NLP infrastructure to 30,000 concurrent outbound calls and 1,000 req/sec - Automated EMI debt collection for TVS Credit & Bajaj Life Insurance — boosted monthly collection by 45%+ - Built TTS systems for Kannada and Hindi using Tacotron2 + WaveGlow — 900ms inference on CPU My Technical Stack - LLMs & Fine-tuning: vLLM · DPO · LoRA · Llama · Mistral · Cohere · OpenAI · NVIDIA NeMo - Speech AI: ASR · TTS (Tacotron2, WaveGlow) · Backchanneling · Indic Language Models · LiveKit - RAG & Agents: LangChain · Crew AI · LangGraph · OpenAI · Llama-3.1 · Extractive QnA - Infra: vLLM · Docker · CUDA · Flask · Python · Multi-GPU Training What I'm Passionate About: I believe the next breakthrough in AI isn't just smarter models — it's AI that truly listens and understands human voice in every language, dialect, and context. India has 22+ official languages and over a billion people who deserve world-class voice AI. I'm building that.

Location

Bengaluru

Work experience

Sr Staff AI Engineer at gnani.ai, Jun 2026 - Present · 3 mos, Bangalore Urban

Lead NLP Engineer at gnani.ai, May 2024 - Jun 2026 · 2 yrs 2 mos, Bengaluru, - Contributed to gnani.ai's Multimodal Speech Audio LLM — integrated acoustic encoder representations with large language model layers to enable context-aware, real-time spoken language understanding at enterprise scale [https://www.cnbctv18.com/technology/bengaluru-based-gnani-ai-unveils-voice-to-voice-ai-model-at-india-ai-summit-ws-l-19853427.html] - Optimized voice bot end-to-end latency to under 2 seconds for Indic languages through enhanced orchestration pipeline and inference engine tuning (NVIDIA NeMo + LiveKit) - Led a cross-functional team of 5 NLP engineers to optimize call center workflows for a U.S. Obamacare health insurance client — significantly reduced Average Handle Time (AHT) and improved agent efficiency - Designed and deployed real-time LLM-based agent assist system: dialog summarization, automatic note creation, and context-aware suggestions using an open-source LLM - Architected a Retrieval-Augmented Generation (RAG) system to streamline agent information retrieval, reducing manual lookup time for insurance domain queries - Fine-tuned LLM using DPO (Direct Preference Optimization) and deployed via vLLM — achieved 35% improvement in suggestion accuracy with 3x cost savings vs. OpenAI API - Drove Multilingual LLM roadmap for Indic language support across Hindi, Bengali, Kannada, and Tamil voice bot deployments

Senior NLP Engineer at gnani.ai, Aug 2021 - May 2024 · 2 yrs 10 mos, Bengaluru, Senior NLP Engineer – Collaborated with product managers, end clients, backend and frontend engineers, and planned deployments while driving team success in developing innovative NLP solutions. - Developed a hybrid LLM/NLU chatbot architecture with intelligent semantic routing, leveraging cosine similarity to route queries and achieving a 90% reduction in latency for high-confidence intents in the insurance domain. - Built an end-to-end RAG-based knowledge base system using OpenAI, Llama-3.1, and Cohere models. Incorporated extractive QnA capabilities and efficient caching mechanisms to optimize question answering. - Implemented an LLM-driven workflow co-pilot using Crew AI, generating workflow graph structures from script or descriptive input, reducing bot development time by an estimated 20%. - Enhanced the user experience of LLM-based voice bots by implementing backchanneling and filler word injection models powered by a DeBERTa-based classifier, reducing first-token latency from 150ms to 50ms. - Improved test coverage by 80% and reduced testing time by 60% for LLM chatbot testing through a lang-graph based agentic system that automatically generates diverse multi-lingual test cases and conversations, including edge cases. - Developed a campaign manager using a Python backend to automate outbound calling, scaling to more than 30,000 concurrent calls.

Assistant System Engineer Trainee at Tata Consultancy Services, Jun 2018 - Nov 2019 · 1 yr 6 mos, Pune Area, India, Support and Enhancement for Banking Application on Documentum

Education

Bachelor's degree, Mechanical Engineering, Siddaganga Institute of Technology

Machine Learning Nanodegree, Information Technology, Udacity