---
title: "Amit Ranjan - Machine Learning Engineer in Bengaluru | Tal"
description: "Meet Amit Ranjan, Applied ML Engineer · Speech AI | Fine-tuning ASR/TTS · Multilingual NLP · Inference Optimization | AWS AI&ML Scholar | Ex-IIT BHU | Bengaluru based in Bengaluru. AI stack includes anthropic apis, antigravity, and cursor."
image: "https://tal.club/meet/amit-ranjan-3/share-image"
---

# Amit Ranjan on Tal

## Headline

Applied ML Engineer · Speech AI \| Fine\-tuning ASR/TTS · Multilingual NLP · Inference Optimization \| AWS AI&ML Scholar \| Ex\-IIT BHU \| Bengaluru

## About

🚀 Machine Learning Engineer \| Speech AI \| NLP \| Generative AI I build production\-grade AI systems at the intersection of Speech, Language, and Generative AI—from fine\-tuning ASR/TTS models to optimizing inference pipelines and deploying scalable ML products used by real users\. At Voicing\.ai, I improved multilingual speech translation quality by \+19 BLEU points through selective encoder\-layer freezing, custom Focal \+ Coverage loss functions, and domain\-adaptive training\. I also developed transcription reliability pipelines that significantly reduced hallucinations using n\-gram blocking, repetition penalties, and word\-level confidence scoring\. At Sarvam AI, I reduced TTS streaming latency from several seconds to <500ms through model quantization and architecture optimization, achieving a 3–4× speedup\. I also deployed production\-scale Whisper ASR systems on GCP using NVIDIA Triton and Ray Serve\. Core Expertise • Speech Recognition \(ASR\) • Text\-to\-Speech \(TTS\) • Speech Translation • NLP & LLM Fine\-Tuning • RAG Systems & AI Agents • Model Optimization & Quantization • Distributed Inference & MLOps • Production ML Systems Tech Stack PyTorch • Hugging Face • Transformers • vLLM • ONNX • NVIDIA Triton • Ray Serve • FastAPI • LangChain • AWS SageMaker • Docker • GCP I focus on building AI systems that perform not only in research benchmarks but also in production, evaluating models across WER, CER, BLEU, MOS, throughput, and P50/P75/P99 latency\. 🏆 AWS AI & ML Scholar 2023 — Ranked among the Top 10 of 1,000 scholars across 80 countries\. 📍 Open to opportunities as a Machine Learning Engineer, Applied Scientist, NLP Engineer, Speech AI Engineer, Research Engineer, or AI Engineer in Bengaluru \(Hybrid/On\-site\)\.

## Location

Bengaluru

## AI stack

anthropic apis

antigravity

cursor

elevenlabs

grok

hugging face

## Work experience

Machine Learning Engineer at Voicing AI, Sep 2025 \- Present · 1 yr, Bengaluru, •Fine\-tuned multilingual Speech\-to\-Text translation models using selective encoder\-layer freezing and custom Focal \+ Coverage loss, improving Synthetic BLEU by \+19\.46 pts \(40\.12 → 59\.58\) and FLEURS BLEU by \+13\.32 pts \(23\.67 → 36\.99\)\. •Designed a transcription reliability pipeline combining probability\-based filtering, teacher\-forced second\-pass validation, and word\-level confidence scoring — reducing hallucinations via n\-gram blocking, repetition penalties, and beam\-search optimization\. •Benchmarked 10\+ open\-source ASR/TTS models at production scale on high\-end GPUs across WER, BLEU, CER, MOS, throughput, and P50/P75/P99 latency to drive model selection and deployment decisions\. •Improved TTS quality and accent neutralization through domain\-adaptive training on targeted speech corpora\.

AI Developer at SarvM\.AI, Oct 2024 \- Aug 2025 · 11 mos, Bengaluru, • Optimized a real\-time TTS inference pipeline using model quantization and architecture tuning, achieving a 3–4× inference speedup and reducing WebSocket audio streaming latency from several seconds to 400–500ms\. Fine\-tuned Whisper Large v3 on a custom domain\-specific dataset using QLoRA \(Quantized Low\-Rank Adaptation\) \+ SFT, improving transcription accuracy while reducing GPU memory footprint for production deployment\. • Deployed production Whisper on GCP using NVIDIA Triton Inference Server \+ Ray Serve, maximizing GPU utilization and enabling high\-concurrency real\-time transcription\. • Integrated speech recognition pipeline with Android and iOS apps, achieving a 20× efficiency improvement vs\. prior LLM\-based automation\.

AI/ML Engineering Intern at SarvM\.AI, Jun 2024 \- Oct 2024 · 5 mos, • Generated synthetic audio data for multiple Indian language accents to expand speech recognition training datasets\. • Supported model deployment pipelines ensuring smooth integration and operational stability\.

Data Science Intern at iNeuron\.ai, Mar 2023 \- Sep 2023 · 7 mos, Bengaluru, • Built and deployed CorrSpell — an end\-to\-end NLP spelling correction tool — from design docs through production deployment\. • Authored architecture and design documentation; created user\-facing wireframes following industry standards\.

AWS AI & ML Scholarship Recipient at Amazon Web Services \(AWS\), Oct 2022 \- Oct 2023 · 1 yr 1 mo, Remote, • Successfully led and executed various machine learning projects, delivering substantial accuracy improvements in bike sharing demand prediction, handwritten digits classification, landmark Classification & tagging for social media, and developed a comprehensive ML workflow for Scones Unlimited on AWS SageMaker\. • I Was among the top 10 finish\-line winners in the first cohort of 1000 AWS AI and ML scholars from 80 countries , and for that, I was chosen to get a reward\.

## Education

B\-Tech, Computer Science and Engineering, Rajiv Gandhi Institute of Petroleum Technology \(RGIPT\)

Ceramic Sciences and Engineering, Indian Institute of Technology \(Banaras Hindu University\), Varanasi

[View the original profile](https://tal.club/meet/amit-ranjan-3)

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