---
title: "Tejas Taware - Artificial Intelligence Intern in India | Tal"
description: "Meet Tejas Taware, AI Engineer | Multi-Agent Systems · RAG · LLMs | Built End-to-End GenAI Pipelines | Pune · Open to Relocate based in India. AI stack includes antigravity and claude. Explore public work links including github."
image: "https://tal.club/meet/tejas-taware/share-image"
---

# Tejas Taware on Tal

## Headline

AI Engineer \| Multi\-Agent Systems · RAG · LLMs \| Built End\-to\-End GenAI Pipelines \| Pune · Open to Relocate

## About

I'm Tejas — an AI/ML Engineer specializing in Generative AI, Agentic Systems, and RAG pipelines\. I've engineered end\-to\-end ML solutions across classification, NLP, and deep learning during internships at Rubixe and YBI Foundation, achieving up to 91% model accuracy on structured datasets\. My flagship project: a Multi\-Agent RAG system built with LangGraph, ChromaDB, Groq, and Streamlit — a production\-grade agentic architecture that routes, retrieves, and reasons across knowledge bases autonomously\. What I bring: → ML pipelines: Scikit\-learn · TensorFlow · Keras → GenAI stack: LangChain · LangGraph · ChromaDB · Groq → Deployment: FastAPI · Streamlit · Python → Data: EDA · Feature Engineering · ARIMA · LSTM B\.E\. in Information Technology \(2025\) \+ DataMites AI Engineer Program \(2026\)\. I'm actively seeking roles in Generative AI / AI\-ML Engineering where I can build systems that don't just predict — they act\. 📩 DM me or reach out at tejastaware2003@gmail\.com

## Location

India

## AI stack

antigravity

claude

## Work experience

Artificial Intelligence Intern at Rubixe, Aug 2025 \- May 2026 · 10 mos, Remote, •Accomplished a 30% reduction in data noise impact by designing and executing full EDA \+ missing value imputation pipelines across 5\+ structured datasets, resulting in measurably cleaner feature sets and improved downstream model convergence\. •Accomplished up to 91% classification accuracy by training and hyperparameter\-tuning Logistic Regression, Random Forest, and SVM models with Scikit\-learn using stratified k\-fold cross\-validation, resulting in deployment\-ready pipelines with optimized precision, recall, and F1\-scores\. •Accomplished regularized deep learning models by implementing dropout layers and batch normalization in TensorFlow/Keras neural networks, resulting in reduced overfitting and improved generalization on held\-out test sets\. •Accomplished model selection for production use by running comparative benchmarks across 5\+ algorithms on identical validation splits, resulting in a reproducible, documentation\-backed model selection framework adopted for final deployment pipelines\.

Data Science Intern at YBI Foundation, Feb 2024 \- Mar 2024 · 2 mos, Remote, •Accomplished improved prediction baselines by building and evaluating classification and regression models using Scikit\-learn with performance benchmarking against baseline heuristics, resulting in quantifiably better F1 and RMSE scores on real\-world datasets\. • Accomplished actionable business insights by collaborating cross\-functionally to analyze complex datasets and surface data\-driven recommendations, resulting in structured deliverables presented to senior team members within a 2\-month sprint\.

## Education

Vidya Pratishthans College of Engineering, M\.I\.D\.C\.road, Baramati, Dist\.Pune

Bachelor of Engineering, Information Technology, CESA VPKBIET

## Public links

- github: https://github\.com/Tejass1303

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

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