I build production features and ruthless automation: LLM-powered products, agentic workflows, and tools that thrive where the documentation runs out.
background & focus
Software engineer and researcher drawn to building scalable systems and quantum machine learning models. My work spans hybrid quantum-classical AI pipelines, full-stack web applications, and statistical monitoring frameworks.
On record: Full Stack Engineering Intern developing marketplaces, SDE intern building LLM automation workflows, and Quantum AI Research Intern benchmarking machine learning on simulated quantum hardware.
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 Quantum engineering challenges.
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.
Conducting research on benchmarking QML models for 3D computer vision and geometric learning, designing hybrid quantum-classical AI pipelines, and developing scalable benchmarking frameworks.
Owned end-to-end development of the InnoPort Marketplace, deployed serverless cloud infra, integrated payment modules, and optimized performance.
Developed LLM-powered automation pipelines, built unified monorepo system, and optimized AWS cloud infrastructure with regression alerting.
End-to-end performance monitoring and statistical anomaly detection pipeline.
Architected an end-to-end automated monitoring pipeline in Python that ingests multi-dimensional time-series performance metrics. Implemented a statistical baseline modeling framework and anomaly detection algorithms to reduce mean time to detection by 60%.
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.
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.
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.
tools & technologies