---
title: "Divanshu S. - Founding Engineer in Mumbai | Tal"
description: "Meet Divanshu S., Founding Engineer | ML Researcher at Yale | Open to AI/ML and founding engineer roles based in Mumbai. AI stack includes claude code, claude, and codex. Explore public work links including github."
image: "https://tal.club/meet/divanshu-s/share-image"
---

# Divanshu S\. on Tal

## Headline

Founding Engineer \| ML Researcher at Yale \| Open to AI/ML and founding engineer roles

## About

I build AI products and ML systems—from model internals to production infrastructure\. As Founding Engineer at Uniiq\.ai, I own an AWS\-deployed admissions platform serving 250\+ users\. I rebuilt conversational onboarding with structured LLM outputs, retries, rate limits, persisted state, and role\-aware API protection; strengthened data/admin workflows; and improved Lighthouse performance from 73→93, LCP from 3\.3s→0\.7s, and INP to 130ms\. Previously, at Yale University, I developed privacy\-preserving CNN components from first principles in Python/NumPy for Multi\-Party Computation, achieving 88\.08% accuracy on ChestMNIST\. I also publish tested, from\-scratch ML systems: a Whisper\-style ASR stack, a PEFT library, a Rust ONNX inference server, and an HNSW vector index\. Earlier, I automated 15\+ quantitative\-research workflows at WorldQuant and ranked Top 1% globally in the Global Alphathon\. Focus: Applied AI, ML systems, LLM inference, retrieval, secure AI, PyTorch, FastAPI, TypeScript, and Rust\.

## Location

Mumbai

## AI stack

claude code

claude

codex

opencode

## Work experience

Founding Engineer at Uniiq, May 2026 \- Present · 4 mos, Mumbai, AI\-powered university admissions platform helping students discover universities, build application profiles, and manage admissions decisions\. \- Led engineering for an AWS\-deployed AI admissions platform, owning architecture, AI integrations, frontend, backend, infrastructure, and deployments\. \- Rebuilt onboarding as a 3\-phase AI conversation with adaptive follow\-ups, transcript persistence, structured validation, rate limiting, and recoverable Gemini failures\. \- Implemented LLM security controls, role\-aware API protection, data\-integrity safeguards, and admin workflows for institutions and opportunities\. \- Improved performance score from 73→93, reduced LCP from 3\.3s→0\.7s, and achieved 130ms INP through code splitting, bundle optimization, compression, and caching\.

Dev Ambassador at MiniMax, Nov 2025 \- Feb 2026 · 4 mos, \- Built technical proofs\-of\-concept and API integrations demonstrating generative AI platform capabilities\. \- Created developer documentation and tutorials to reduce integration friction and improve developer onboarding\. \- Served as a technical liaison between external developers and the product team, converting community feedback into actionable developer\-experience improvements\.

Machine Learning Researcher at Yale University, Oct 2025 \- Mar 2026 · 6 mos, Remote, \- Developed privacy\-preserving deep\-learning components from first principles in Python/NumPy, without external autograd, for training CNNs under Multi\-Party Computation\. \- Designed a custom CNN that achieved 88\.08% test accuracy on ChestMNIST while operating over cryptographically protected data\. \- Authored Sequre contributions covering Conv2D, CNN layers, encrypted pooling tests, and a ChestMNIST MPC training pipeline\.

Machine Learning Researcher at AI Pioneers \(aipioneers\.me\), Oct 2025 \- Mar 2026 · 6 mos, Remote, \- Developed privacy\-preserving deep\-learning components from first principles in Python/NumPy, without external autograd, for training CNNs under Multi\-Party Computation\. \- Designed a custom CNN that achieved 88\.08% test accuracy on ChestMNIST while operating over cryptographically protected data\. \- Authored Sequre contributions covering Conv2D, CNN layers, encrypted pooling tests, and a ChestMNIST MPC training pipeline\.

Quant Research Consultant at WorldQuant, Nov 2022 \- Jul 2025 · 2 yrs 9 mos, \- Automated 15\+ quantitative signal\-generation and backtesting workflows using Python across multiple global markets\. \- Developed strategies with a documented average simulated Sharpe ratio of 1\.8 and ranked Top 1% globally in the 2022 Global Alphathon\.

## Education

Bachelor of Engineering \- BE, Computer Science, University of Mumbai

Global Summer School, Quantum Machine Learning, Quantum Simulation, Qiskit \(by IBM Quantum\)

## Public links

- github: https://github\.com/sdivyanshu90

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

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