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ROHIT GIRISHBELAGALI

CAPTRKBASE

I build production features and ruthless automation: LLM-powered products, agentic workflows, and tools that thrive where the documentation runs out.

View case filesTransmit
TitleMACHINE LEARNING · SOFTWARE ENGINEERING
Drawn byRohitGB
Scale1:1
Rev2.0
Date----.--.--
Dwg NoROHITGB-2026-001
Observers · live/ --
01 / ABOUT

ABOUT

background & focus

subjectRohitGBlocation12.9716°N 77.5946°EstatusOperational

Software engineer and researcher drawn to building scalable systems and machine learning models. The work splits between engineering cloud-native data infrastructure and exploring cutting-edge quantum machine learning algorithms.

On record: SDE intern developing LLM-powered automation, ML research intern engineering blockchain-enabled tracking systems, and author of peer-reviewed research in Quantum Machine Learning.

Driven by a stubborn curiosity about how complex systems behave under the hood, and the patience to design robust pipelines. Looking to collaborate on high-impact backend, ML, or Web3 engineering challenges.

3+
products shipped end-to-end
1
peer-reviewed QML publication
99%+
uptime on optimized data pipelines
3
completed engineering internships
02 / CAPABILITIES

CAPABILITIES

areas of focus

Building reliable full-stack applications and cross-platform solutions. Shipped web and mobile interfaces using React, Flutter, Node.js, and Python, while establishing unified monorepo structures and CI/CD pipelines.

  • React
  • Flutter
  • Node.js
  • Python
  • Monorepos
  • CI/CD

03 / EXPERIENCE

Where I’ve shipped

  1. [2024–PRES]Bengaluru, India · Nov 2024 — Present

    ML Research Scientist Intern / AgForde

    Developed cloud-native data infrastructure processing large-scale cross-border transaction datasets and engineered blockchain supply chain tracking systems.

    • Developed cloud-native data infrastructure processing large-scale transaction datasets with ETL pipelines and real-time dashboards.
    • Engineered blockchain-enabled supply chain tracking systems ensuring transparency across global import-export networks.
    • Optimized predictive ML models for demand forecasting and logistics, reducing operational costs by 44%.
    • Established fault-tolerant data pipelines achieving 99% uptime for mission-critical monitoring systems.
    • ETL Pipelines
    • APIs
    • Blockchain
    • Predictive ML
    • Data Pipelines
  2. [2025]Bengaluru, India · May 2025 — Aug 2025

    SDE Intern / Tradyon.ai

    Led intern team, built unified monorepo system, and developed LLM-powered automation solutions to cut manual workloads.

    • Built unified monorepo system for React, Flutter, Node.js, and Python, reducing code duplication by 30% and streamlining CI/CD.
    • Developed and deployed LLM-powered automation solutions (Diffie chat, N8n workflows) cutting manual workload by 40%.
    • Launched cloud-native distribution system (Flutter + Firebase + AWS) supporting 10k+ transactions and reducing deployment time by 25%.
    • Optimized AWS infrastructure achieving 99.9% system uptime under high-volume workloads.
    • React
    • Flutter
    • Node.js
    • Python
    • LLM Automation
    • AWS
  3. [2024]Noida, India · Jan 2024 — Jun 2024

    SDE Intern / AppSquadz Software

    Developed logistics web platform and Python backend services, streamlining international shipping operations.

    • Developed logistics web platform streamlining shipping operations and boosting efficiency by 40%.
    • Deployed Python backend services and React.js frontend with Dockerized applications, reducing deployment effort by 35%.
    • Implemented advanced routing algorithms decreasing shipping errors by 30% and improving delivery rates.
    • Integrated real-time analytics dashboards enabling data-driven decision-making across operations.
    • Python
    • React.js
    • Docker
    • Routing Algorithms
    • Dashboards
04 / SELECTED WORK

Things I’ve built

01
On-chain / Low-latency/Solana & Ethereum/2024

Decentralized Copy Trading Bot

Low-latency copy trading bot for Solana & Ethereum DEXs.

Built an automated copy trading bot with transaction decoding and Uniswap DEX integration. Optimized execution algorithms to minimize slippage and built a custom analytics dashboard for portfolio tracking.

  • Decoded swap input data in real-time to mirror transactions across Uniswap and Solana DEXs.
  • Optimized transaction routing and execution paths to execute orders within the same/next block, keeping slippage under 0.5%.
  • Designed a dashboard for portfolio performance, tracking win-rates, profit/loss metrics, and gas consumption.
Minimized slippage via block-level transaction execution
SolanaEthereumWeb3UniswapSolidityLow-Latency
02
Concurrency / Automation/Independent Project/2023

E-commerce Flash Sale Automation Bot

Multi-threaded flash sale automation system.

Engineered a high-demand automation system for flash-sale retail launches. Features real-time web scraping for price monitoring, live inventory tracking, and concurrent multi-account checkout pipelines.

  • Built a multi-threaded request-driven checkout engine capable of simultaneous bypass of cart holds.
  • Implemented low-overhead web scraping algorithms for microsecond price and stock monitors.
  • Designed robust anti-bot bypass mechanisms to handle high-demand platforms under load.
Simultaneous checkouts across multiple platforms
PythonNode.jsConcurrencyWeb ScrapingAnti-Bot Bypass
03
Lead Research Author/IJNRD Publication/2024

Quantum Machine Learning

Exploring Quantum Machine Learning for enhanced data processing.

Published a peer-reviewed research paper titled "Exploring the Potential of Quantum Machine Learning for Enhanced Data Processing" (ISSN: 2456-4184). Investigated QML convergence using superposition, qubits, quantum gates, feature maps, and quantum kernels.

  • Published peer-reviewed paper in IJNRD demonstrating quantum-inspired algorithms outperforming classical ML on high-dimensional data.
  • Developed quantum data encoding techniques, quantum feature maps, and quantum kernels; implemented QSVM and QNNs.
  • Analyzed QML applications across finance, healthcare, and cryptography with exponential speedup over classical methods.
Published peer-reviewed paper in IJNRD
Quantum MLQiskitPennyLanePythonQNNQSVM
04
Independent Researcher/Ongoing Research/2025

Quantum Kernel Methods

Hybrid quantum-classical ML approaches combining quantum kernel methods.

Investigating hybrid quantum-classical ML approaches combining quantum kernel methods with classical preprocessing for pattern recognition. Developed novel algorithms leveraging quantum feature spaces with PCA/t-SNE to optimize computational efficiency.

  • Built hybrid architectures integrating quantum circuits with scikit-learn pipelines.
  • Explored applications in financial time-series prediction and anomaly detection showing 30-40% improvements over classical methods.
  • Implemented quantum circuits using Qiskit and PennyLane frameworks.
Ongoing research manuscript in preparation
Quantum KernelsQiskitPennyLaneScikit-LearnPCA / t-SNE
05 / SKILLS

SKILLS

tools & technologies

[A]

RPA, Automation & RE

  • RPA / Bot Frameworks
  • API / Android Reverse Engineering
  • Puppeteer
  • Appium
  • SSL Proxying
  • Burp Suite
  • Frida
  • Dify
[B]

Frontend

  • React
  • Next.js
  • TypeScript
  • Tailwind
  • React Native
[C]

Backend & DB

  • Node.js
  • Python
  • PHP
  • PostgreSQL
  • MongoDB
[D]

Cloud & Infra

  • GCP · Compute · Cloud Run
  • AWS · ECS · Lambda
  • Docker
  • CI / CD Pipelines
  • Nginx · Reverse Proxy
  • Linux · VPS Ops
06 / CONTACT

CONTACT

response time: fast

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