---
title: "Priangshu Paul - Machine Learning Engineer in Bengaluru | Tal"
description: "Meet Priangshu Paul, Building Efficient AI Systems | Large Language Models • Distributed Training • GPU Optimization based in Bengaluru. AI stack includes claude code, cursor, and grok. Explore public work links including github."
image: "https://tal.club/meet/priangshu-paul/share-image"
---

# Priangshu Paul on Tal

## Headline

Building Efficient AI Systems \| Large Language Models • Distributed Training • GPU Optimization

## About

I like to research and build stuff

## Location

Bengaluru

## AI stack

claude code

cursor

grok

hugging face

langchain

mcp

## Work experience

Machine Learning Engineer at Nuroen\.AI, Jul 2026 \- Present · 3 mos, Bengaluru, Karnataka, India, Fine\-tuned a 32B model on 8×A100 over a 2\.05M\-sample instruction corpus I built and decontaminated myself: \+24 pts instruction\-following, \+16 grounded refusal, \+4 tool\-calling — and I reported the −8\.5 MMLU\-Pro regression alongside them, because an eval that only publishes gains is marketing\. Built a paired A/B harness to test whether retrieving auto\-generated agent tool\-skills actually helps: 1,088 execution\-verified tasks → 93\.5% to 99\.0% success \(\+5\.5pp, McNemar p<0\.001\), then shipped a retrieval gate that held the entire win at 87% lower token cost\. Validated that against the 50\+ real production connectors \(181 actions\) — and the synthetic numbers didn't hold: retrieval found the right tool 100% of the time but ranked it first only 68%\. The run caught a bug in my own shipped default, silently dropping the correct tool 18\.8% of the time\. Fixed before it reached production\. Removed the scaling wall on a real\-time STT→LLM→TTS voice agent: latency flat 1\.3–1\.8s from 1→128 concurrent \(replacing a stack that ballooned 161ms → 3\.3s\), sustained soak of 240/240 turns at p95 215ms, and accent parity at 8\.4% WER for Indian\-accented English vs 7\.4% for US\. Migrated a 1\.14M\-chunk knowledge base to Postgres \+ pgvector: −49% storage, 4\.4× faster, zero re\-embedding — and proved it safe rather than assuming \(top\-1 92% parity vs exact search\), which surfaced two real bugs including a tuning parameter that was silently a no\-op\. Made the vector store swappable and proved it: pgvector vs Qdrant recall identical to 4 decimal places\. The commercially useful conclusion — retrieval quality is the embedder and chunking, not the vendor — so no lock\-in\.

Data Processing Intern at CHRIST University, Yeshwanthpur Campus \(BYC\), Jan 2026 \- Mar 2026 · 2 mo, Bengaluru, Karnataka, India, Conducted research in LLMs focusing on bias evaluation and mitigation in low\-resource indian languages

Summer Intern at Calcutta Mathematical Society, Jun 2023 \- Aug 2023 · 3 mos, Kolkata, West Bengal, India

## Education

Master of Science, Data Science, Christ University, Bangalore

Bachelor of Science, Mathematics, Amity University Kolkata

## Public links

- github: https://github\.com/priangshu17

[View the original profile](https://tal.club/meet/priangshu-paul)

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