---
title: "Mohaneesh Pradhan - SWE intern in Bengaluru | Tal"
description: "Meet Mohaneesh Pradhan, Former SWE Intern @ Google | M.E. Software Systems ’26 @ BITS Pilani, Pilani Campus based in Bengaluru. AI stack includes antigravity and codex. Explore public work links including github and leetcode."
image: "https://tal.club/meet/mohaneesh-pradhan/share-image"
---

# Mohaneesh Pradhan on Tal

## Headline

Former SWE Intern @ Google \| M\.E\. Software Systems ’26 @ BITS Pilani, Pilani Campus

## About

I am proficient in C\+\+, python and SQL\. I possess hands\-on knowledge of machine learning, deep learning, and computer vision, along with cross\-platform app development using Flutter\. Additionally, my strong foundation in Data Structures and Algorithms, particularly in C\+\+, allows me to approach problem\-solving in a systematic and efficient manner\.

## Location

Bengaluru

## AI stack

antigravity

codex

## Work experience

SWE intern at Google, Jan 2026 \- Jul 2026 · 7 mos, Bangalore Urban, During my time as a Software Engineering Intern at Google, I spearheaded the development of a Gen\-AI Agent Orchestration System and an accompanying Observability App designed to enable intelligent service discovery and streamline complex deployment workflows\. I architected a robust integration framework utilizing Python, the Agent Development Kit \(ADK\), and WebSockets to evaluate eight distinct agent skills, while also building a TypeScript and REST\-based internal observability application\. Ultimately, this system drove significant efficiency gains by achieving a 50% reduction in manual testing through automation, and the app served as a crucial reference implementation to resolve ongoing client issues\.

Teaching Assistant at Birla Institute of Technology and Science, Pilani, Aug 2024 \- Dec 2025 · 1 yr 5 mos, Pilani, Rajasthan, India

Software Engineer Intern at Navikenz, Jan 2024 \- Jul 2024 · 7 mos, Remote, In my remote role at Navikenz, I focused heavily on applied machine learning by developing an Audio\-based Sentiment Classification System capable of categorizing speech into seven distinct emotional classes\. I engineered and trained various deep learning architectures—including CNN, LSTM, and hybrid CNN\-LSTM models—leveraging a comprehensive dataset of over 20,000 audio samples sourced from TESS, RAVDESS, CREMA, and SAVEE\. My efforts culminated in a highly effective solution, attaining a peak validation accuracy of 98% with the CNN model, which successfully outperformed both the standalone LSTM and the combined CNN\-LSTM architectures\.

Project Intern at Tata Group, Jun 2022 \- Aug 2022 · 3 mos, Jamshedpur, As a Project Intern at Tata Steel in Jamshedpur, I developed a comprehensive Dispatch Operations Analytics App using Flutter to actively enhance data\-driven decision\-making capabilities for the road dispatch team\. To support the mobile interface, I implemented a robust Flask backend and analytics pipelines designed to process and visualize real\-time data tracking 700 to 1,000 vehicles and 17 kilotons of daily dispatch\. By optimizing the automatic data ingestion, processing, and reporting workflows, this application successfully yielded a 60% reduction in manual effort for the dispatch personnel\.

## Education

Master of Engineering \- MEng, Software Systems, Birla Institute of Technology and Science, Pilani

Bachelor of Technology \- BTech, Information Technology, Manipal University Jaipur

## Public links

- github: https://github\.com/mohaneesh\-03
- leetcode: https://leetcode\.com/mohaneesh33rd

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

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