--- title: "Sneh Shah - Machine Learning Engineer in Bengaluru | Tal" description: "Meet Sneh Shah, Machine Learning Engineer | Data Scientist | Speech AI (ASR/TTS) + LLM Agent Systems based in Bengaluru. AI stack includes claude, hugging face, and mcp. Explore public work links including github and sneh2909.github.io." image: "https://tal.club/meet/sneh-shah/share-image" --- # Sneh Shah on Tal ## Headline Machine Learning Engineer \| Data Scientist \| Speech AI \(ASR/TTS\) \+ LLM Agent Systems ## About I build the listening half of voice AI — the ASR and agent layer that has to work before a voice bot can say anything useful\. At ConvoZen\.ai \(NoBroker\) I train multilingual Conformer ASR across 9 Indian languages on 10,000\+ hours and run it in production on Triton Inference Server and NVIDIA Riva at sub\-second transcription\. Around the model sits the harder half: a multi\-session conversational harness with MCP tool\-calling, multi\-LLM routing, and shared state across voice, chat and email\. Before this at Salesken\.ai I fine\-tuned NeMo's cache\-aware FastConformer Hybrid into a streaming Hindi ASR model with 0–1040 ms configurable look\-ahead, replaced a paid third\-party API with it, and cut monthly transcription cost by 20%\. It's open on Hugging Face\. 🔧 What I'm working on • Multilingual ASR — distributed multi\-GPU training with CTC / RNNT / TDT decoders and hybrid joint\-loss objectives across 9 Indian languages, served on NVIDIA Riva • Agentic conversation systems — MCP tool\-calling, multi\-LLM routing with fallbacks, shared state across channels, 80% query\-resolution rate across enterprise deployments • Retrieval & memory — agentic chunking that pushed Recall@1 from 63% to 80% and MRR from 0\.73 to 0\.87; rolling\-window summarization with hybrid retrieval cutting token spend 30–40% without losing coherence • RL post\-training \(nights and weekends\) — a POMDP clinical\-reasoning environment where I post\-trained Gemma\-3\-4B into a sequential diagnostician via SFT plus a custom GRPO loop, with a 5\-rubric reward built to resist reward hacking\. 6 checkpoints and 2 datasets open\-sourced, OpenEnv\-compatible\. 🧠 Stack I work in Speech & ASR: Conformer / FastConformer, CTC / RNNT / TDT, NeMo, Whisper, speaker diarization, VAD Serving: NVIDIA Riva, Triton, TensorRT\-LLM, vLLM, ONNX, Docker, Kubernetes LLM & Agents: MCP, multi\-LLM orchestration, RAG, hybrid retrieval, vector DBs, PEFT, TRL / Unsloth Systems: Python, FastAPI, Kafka, PostgreSQL, MongoDB, Azure, Prometheus 🧭 How I work I'd rather read the paper and understand the tensor shapes than wire together someone else's abstraction\. I care about what a system does at p99 under real traffic, not what it does in a demo\. Most of the interesting work in voice AI lives in the gap between "the model is accurate" and "the conversation felt fine" — and that gap almost never closes with a better checkpoint\. Open to conversations on speech AI, Indic ASR, voice agent infrastructure, and Inference Optimization\. ## Location Bengaluru ## AI stack claude hugging face mcp notebooklm notion vLLM ## Work experience Machine Learning Engineer at NoBroker\.com, Jul 2025 \- Present · 1 yr 4 mos, Trained multilingual ASR models on 10,000\+ hours across 9 Indian languages using distributed multi\-GPU training\. Deployed multilingual ASR on NVIDIA Riva, delivering sub\-second transcription and reducing production inference cost\. Built a Memory Management Service using rolling\-window summarization and hybrid retrieval, cutting token usage by 30–40% while preserving conversational coherence across long sessions\. Engineered a production, multi\-session conversational\-AI harness with MCP tool\-calling, multi\-LLM routing, and shared state management across voice, chat, and email, achieving an 80% query\-resolution rate across enterprise deployments\. Boosted Recall@1 from 63% to 80% and MRR from 0\.73 to 0\.87 on an internal retrieval benchmark with an agentic chunking pipeline for RAG\. Machine Learning Engineer at Salesken, Apr 2024 \- Jul 2025 · 1 y 3 mo, On\-site, • Built a Retrieval\-Augmented Generation \(RAG\) system to store and retrieve organizational data, enabling personalized, data\-driven solutions and improving query resolution speed by 20%\. • Developed a PII masking solution for identifying and protecting sensitive data using advanced techniques like pseudonymization, anonymization, tokenization, and truncation, ensuring compliance with data privacy regulations\. • Engineered a proactive daily alert system for Slack to monitor ASR performance metrics, achieving a 30% reduction in system downtime and enhancing issue resolution processes\. • Optimized and deployed the Whisper\-ASR system on the Triton Inference Server, reducing latency by 25% and enhancing scalability for real\-time applications\. • Designed and implemented a Kafka Controller to manage message workflows efficiently, reduce data duplication by 15%, and ensure idempotency, improving system reliability and performance\. ML Intern at Salesken, Jan 2024 \- Mar 2024 · 3 mos, Bengaluru, Karnataka, India, Raised ASR transcription accuracy by 10% with speaker diarization and voice\-activity\-detection modules, and shipped Emotion Detection models on Triton\. Virtual Internship in Generative AI at Open Weaver, Jun 2023 \- Jun 2023 · 1 mo, Remote, • Developed a customized interface for Chat\-GPT, acquiring hands\-on experience in seamlessly integrating AI language models\. • Utilized Open AI's Whisper and DALL\-E tools to convert audio into visually generated images\. • Applied deep learning algorithms and computer vision methods to detect and mark Deep\-Fake images\. ## Education Master of Science \- MS, Data Science, Christ University, Bangalore Bachelor of Science, Data Science, Gujarat University ## Public links - github: https://github\.com/sneh2909 - sneh2909\.github\.io: https://sneh2909\.github\.io/Portfolio/ [View the original profile](https://tal.club/meet/sneh-shah) ```json { "@context": "https://schema.org", "@graph": [ { "@type": "Person", "@id": "https://tal.club/meet/sneh-shah#person", "name": "Sneh Shah", "url": "https://tal.club/meet/sneh-shah", "image": "https://tal.club/meet/sneh-shah/profile-photo.jpg", "description": "Meet Sneh Shah, Machine Learning Engineer | Data Scientist | Speech AI (ASR/TTS) + LLM Agent Systems based in Bengaluru. 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