---
title: "Tohid Shaikh - Data Analyst — National Hackathon in Bengaluru | Tal"
description: "Meet Tohid Shaikh, Backend & ML Engineer | Distributed Systems · LLM Integration · RAG Pipelines | UIDAI National Hackathon | Open to SWE & AI/ML Roles based in Bengaluru. AI stack includes gemini, langchain, and ollama. Explore public work links including github."
image: "https://tal.club/meet/tohid-shaikh/share-image"
---

# Tohid Shaikh on Tal

## Headline

Backend & ML Engineer \| Distributed Systems · LLM Integration · RAG Pipelines \| UIDAI National Hackathon \| Open to SWE & AI/ML Roles

## About

Backend engineer completing B\.Tech \(Electrical Engineering, GEC Dahod — June 2026\) who pivoted deep into distributed systems, ML pipelines, and LLM integration\. I build things that run in production — not just notebooks\. Projects include a hybrid\-encrypted offline UPI payment mesh \(Flask, RSA\-OAEP, AES\-GCM, optimistic locking\), a vector database built from scratch with HNSW \+ RAG pipeline achieving sub\-200ms ANN latency, a national\-scale analytics engine for UIDAI processing 5M\+ records across 807 districts, a multithreaded Deep Packet Inspection engine parsing Ethernet/IP/TCP/TLS across 77\+ packets, an NLP\-powered meeting intelligence tracker, and an LLM\-powered Python\-to\-Java code migration tool — all with working code and live demos\. Seeking entry\-level Software Engineering or ML Engineer roles where I can contribute to scalable backend systems, AI/LLM products, or data infrastructure\. Currently strengthening DSA and system design\. Self\-taught SWE background built through 6 production\-grade projects\. GitHub: github\.com/T786\-eng

## Location

Bengaluru

## AI stack

gemini

langchain

ollama

openai

## Work experience

Data Analyst — National Hackathon at UIDAI Aadhaar Innovation Challenge 2026, Feb 2026 \- Feb 2026 · 1 mo, Remote, Built Project DRAM \(Dynamic Resource Allocation Model\), a national\-scale analytics engine processing 5M\+ records across 12 data sources and 807 districts\. Implemented Z\-score anomaly detection and a custom UER metric to classify districts into strategic infrastructure zones\. Presented live Streamlit dashboard to a national\-level judging panel\.

ML Engineer Intern at Edunet Foundation, Jan 2026 \- Feb 2026 · 2 mos, Hybrid, Engineered a production\-ready Random Forest classification pipeline on 12,000 records, achieving 91\.2% accuracy and 0\.87 F1\-score\. Designed end\-to\-end modular ML workflows with Pandas and Scikit\-learn, eliminating data leakage and maximizing cross\-environment reproducibility\.

Machine Learning Intern at 3SKill, Dec 2025 \- Jan 2026 · 2 mos, Gujarat, India, Architected a fraud detection backend on 20,000\+ financial transactions\. Applied SMOTE oversampling to lift recall from 61% to 84%, achieving 92% accuracy and 0\.89 ROC\-AUC\. Reduced false\-negative rate by 38% on highly imbalanced real\-world data through precision\-targeted feature engineering\.

## Education

Bachelor of Technology \- BTech, Electrical Engineering, Government Engineering College, Dahod 018

St\. Stephen Sr\. Sec\. School

## Public links

- github: https://github\.com/T786\-eng

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

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