---
title: "Yash Singhal - Systems Engineer Trainee in India | Tal"
description: "Meet Yash Singhal, Backend & GenAI Engineer | Python • Node.js • PostgreSQL • LLMs | Ex-Reliance Jio Intern | VIT Vellore ’26 based in India. AI stack includes antigravity, claude code, and claude. Explore public work links including github."
image: "https://tal.club/meet/yash-singhal-1/share-image"
---

# Yash Singhal on Tal

## Headline

Backend & GenAI Engineer \| Python • Node\.js • PostgreSQL • LLMs \| Ex\-Reliance Jio Intern \| VIT Vellore ’26

## About

I don't just write code \- I build systems that solve real problems\. Last year at Reliance Jio, I built a multi\-agent GenAI assistant from scratch for Telecom APIs\. Before it existed, developers spent 2–3 hours resolving API queries\. After? Under 1 minute\. That's the kind of impact I chase\. I'm a final\-year B\.Tech CSE student at VIT Vellore , specializing in backend engineering and AI/LLM systems\. My stack: Backend: Python, Node\.js, Express\.js, Flask, REST APIs Databases: PostgreSQL, SQLAlchemy, query optimization AI/GenAI: Langflow, RAG pipelines, Qwen/Mistral LLMs, Langfuse, Prompt Engineering DevOps: Docker, Git, Render, Agile sprints Beyond my internship, I've independently shipped two live products: Personal Finance Tracker \(Live\) → https://fj\-be\-r2\-yashsinghal\-vitvellore\-h9q3\.onrender\.com/ SQL Query Optimizer Pro \(Live\) → https://sql\-optimizer\.onrender\.com/ I'm actively looking for Backend, GenAI, Data Engineering, or Full\-Stack roles at product companies and MNCs\. I'm open to relocation and remote opportunities\. Let's build something great\. 🔗 github\.com/Yash19Singhal

## Location

India

## AI stack

antigravity

claude code

claude

codex

cursor

gemini

## Work experience

Systems Engineer Trainee at Infosys Ltd, Aug 2026 \- Present · 1 mo, Mysore

Software Intern at Reliance Jio, Jun 2025 \- Jul 2025 · 1 mo, Navi Mumbai, Maharashtra, India, Built and deployed a production\-grade multi\-agent GenAI assistant for Telecom APIs, reducing developer query resolution time from 2–3 hours to under 1 minute while improving system throughput by 35%\. • Designed a multi\-agent AI pipeline \(Orchestrator, Search, Analysis, Generation\) handling ambiguous natural language queries for Telecom APIs — cutting resolution time from 2–3 hours to under 1 minute\. • Integrated RAG search, OpenAPI parsing, and code generation into a modular orchestration workflow, reducing developer onboarding time by 40% and improving system throughput by 35%\. • Achieved 99% accuracy in API schema extraction using semantic retrieval \(Roadie RAG \+ OpenAPI parsing\), validated across 20\+ live queries from Jio Developer Studio\. • Benchmarked Qwen 2\.5 vs Qwen 3\.14 on Ollama — reduced average response latency by 25% through model selection and prompt optimization\. • Integrated Langfuse observability, cutting troubleshooting effort by 50% and standardizing prompt templates to reduce recurring errors by 25%\. • Collaborated with backend, API, and product teams in Agile sprints — contributed to design reviews, sprint planning, and cross\-functional delivery\.

## Education

Bachelor of Technology \- BTech, Computer Science, Vellore Institute of Technology

## Public links

- github: https://github\.com/Yash19Singhal

[View the original profile](https://tal.club/meet/yash-singhal-1)

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