---
title: "Yashwanth Brahma BM - AI Model Evaluation in Mysore | Tal"
description: "Meet Yashwanth Brahma BM, Agentic AI | AI Engineer | React/React Native + AWS, now building LLM agents, RAG & multi-agent systems (LangGraph, MCP) from scratch | Python, FastAPI | LLM evaluation & prompt-injection defense | Open to Work based in Mysore. AI stack includes anthropic apis, claude code..."
image: "https://tal.club/meet/yashwanth-brahma-bm/share-image"
---

# Yashwanth Brahma BM on Tal

## Headline

Agentic AI \| AI Engineer \| React/React Native \+ AWS, now building LLM agents, RAG & multi\-agent systems \(LangGraph, MCP\) from scratch \| Python, FastAPI \| LLM evaluation & prompt\-injection defense \| Open to Work

## About

I spent three years building React and React Native apps end to end, including the AWS serverless backends behind them\. Then I got curious about what was actually happening inside AI agents — and decided the only way to find out was to build one from scratch\. No LangChain\. Just a loop, a provider\-agnostic client, tools generated from type hints, and every failure mode discovered the hard way\. Then I kept going\. What I've built recently: docagent — a multi\-agent research assistant over \~100 technical PDFs that answers with verifiable citations\. The hard part wasn't the agents; it was ingestion\. Naive RAG flattens a PDF into text, which destroys tables — and in research papers, the answer is usually in a table\. So I parse each document into typed elements and chunk each type differently\. A planner splits questions into sub\-queries, workers search in parallel, and a critic verifies every claim has a source before the answer ships\. miniagent — an LLM agent framework built without an orchestration library: autonomous loop with three independent runaway guards, prompt\-injection\-hardened tools \(I red\-teamed my own agent and found a credential leak in my file\-read tool\), retries, tracing, and measured p50/p95 latency\. Later rebuilt in LangGraph specifically so I could articulate what the framework gives you and what it hides\. How I work: I measure instead of assuming\. When I added hybrid search to my retrieval pipeline, I benchmarked it against a golden set rather than trusting that it helped — and found it's situational, not a free upgrade\. When my agent cited real numbers under the wrong dataset, I traced it through five layers down to how LaTeX floats tables\. That instinct comes from my other work: adversarial evaluation of frontier models, where I plant false premises and verify every claim against source before crediting it\. I'm looking for agentic AI / AI engineering roles where systems have to actually work in production — not just demo well\. Bengaluru or remote\. Python \(FastAPI, asyncio, Pydantic\) · LangGraph · RAG & hybrid retrieval · MCP · agent evaluation · React / React Native · AWS · Docker

## Location

Mysore

## AI stack

anthropic apis

claude code

claude

codex

langgraph

claudecode

## Work experience

AI Model Evaluation at Freelance \| Self\-Employed, Jan 2026 \- Present · 11 mos, Bengaluru, Karnataka, India, Frontier\-model evaluation & adversarial testing

Software Engineer at Urneeds, Apr 2022 \- Jul 2025 · 3 yrs 4 mos, Mysuru, Karnataka, India

## Education

PGDIT, Big data, Jain \(Deemed\-to\-be University\)

Bachelor's degree, Computer Science, Amrita vishwa vidyapeetham,mysore campus

## Public links

- github: https://github\.com/Yashwanth\-Brahma

[View the original profile](https://tal.club/meet/yashwanth-brahma-bm)

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