---
title: "vasavi bokkisam - ai agent dev intern in Bengaluru | Tal"
description: "Meet vasavi bokkisam, ML Engineer | RAG · LLMs · LangGraph · LangChain · Python | Multi-agent Systems · AutoML | Ex-TCS GenAI Intern | Open to Work based in Bengaluru. AI stack includes claude code, gemini, and grok."
image: "https://tal.club/meet/vasavi-bokkisam/share-image"
---

# vasavi bokkisam on Tal

## Headline

ML Engineer \| RAG · LLMs · LangGraph · LangChain · Python \| Multi\-agent Systems · AutoML \| Ex\-TCS GenAI Intern \| Open to Work

## About

I build production\-grade GenAI systems — RAG pipelines, multi\-agent workflows, and AutoML solutions that solve real problems\. Currently at Smartkeen Tech building LLM\-powered multi\-agent systems with LangGraph, reducing decision\-making overhead by \~40%\. Previously at TCS optimizing RAG pipelines over 50K\+ documents, improving retrieval accuracy by 25% and cutting query latency by \~20%\. Stack: Python · LangChain · LangGraph · HuggingFace · ChromaDB · Scikit\-learn · Docker · Groq API Seeking full\-time ML Engineer / Data Scientist roles in India\. Let's connect — vasavibokkisam03@gmail\.com

## Location

Bengaluru

## AI stack

claude code

gemini

grok

hugging face

ollama

## Work experience

ai agent dev intern at Smartkeen Tech, Jan 2026 \- Jun 2026 · 6 mos, Bengaluru, Designed and deployed LLM\-powered multi\-agent systems using LangGraph, reducing manual decision\-making overhead by \~40% in automated business workflows\. Built and evaluated 3\+ end\-to\-end agent pipelines integrating LLM reasoning, tool use, and external REST APIs for intelligent process automation\. Architected agent state management with declarative flow control, cutting failure recovery time by \~30% through modular task delegation\.

Intern at TCS\_India, Jan 2025 \- May 2025 · 5 mos, Hyderabad, Telangana, India, Built RAG\-based QA chatbot using Python, LangChain, and ChromaDB with Transformer\-based embeddings, achieving 25% improvement in retrieval accuracy over baseline\. Optimized chunk size, indexing strategies, and embedding pipelines, reducing average query latency by \~20% in production\-scale QA systems handling 50K\+ documents\. Contributed to vector storage strategies, modular LLM pipelines, and prompt workflows across a team of 6 engineers

## Education

B\.Tech, computer science and data science, R\.V\.R\. & J\.C\. College of Engineering

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

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