Parth Bansal

I'm Parth Bansal, a Python developer with hands-on experience in AI/NLP systems and backend development. I've built projects using FastAPI, LangGraph, LangChain, RAG pipelines, Qdrant vector search, and sentence-transformer embeddings — including an AI-powered e-commerce research agent with multi-step reasoning and automated report generation.

Experience

none

Education

CBSE

12th / Higher Secondary

Percentage: 82%

CBSE

10th / Secondary

Percentage: 88.2%

Skills

PythonC++JavaScriptTypeScriptJavaCReactNode.jsExpress.jsFastAPITailwind CSSZustandPrisma ORMSocket.IONumPyPandasMatplotlibspaCyPyTextRankLangGraphLangChainRetrieval Augmented Generation (RAG)Vector DatabasesPrompt EngineeringEmbeddingsSentence TransformersNatural Language Processing (NLP)PostgreSQLMongoDBMySQLRedisGitDockerVercelREST API DevelopmentJWT AuthenticationData Structures and Algorithms (DSA)Full-Stack Web DevelopmentBackend SystemsEvent-Driven ArchitectureReal-Time Systems (WebSockets)Database DesignAPI DesignContainerizationScalable System Design

Projects

DSA Contest Platform – Secure Code Execution System

Dec 2025 - Jan 2026

Tech: React (TypeScript), Node.js, Express, MongoDB, Docker, Socket.IO

Architected and developed a full-stack competitive coding platform supporting individual practice and team-based contests with real-time leaderboards and submission evaluation.
Engineered a secure Docker-based sandboxed code execution engine with CPU and memory isolation to safely run multi-language programs (C++, Java, Python, C) at scale.
Implemented JWT-based authentication, asynchronous submission queueing, and WebSocket-powered real- time updates to enable scalable and responsive contest execution.

E-Commerce Intelligence Research Agent – AI Analytics Platform

Mar 2026

Tech: FastAPI, LangGraph, PostgreSQL, Qdrant, Redis, React (TypeScript), Tailwind, Docker

Built an AI-powered business analytics platform that analyzes e-commerce data and generates strategic insights using LLM-based research agents and retrieval-augmented generation (RAG).
Designed a multi-step agentic workflow using LangGraph implementing intent classification, contextual memory retrieval, multi-pass analysis, and automated report generation.
Implemented vector-based semantic search using Qdrant and sentence-transformer embeddings to enable contextual data retrieval from product catalogs, reviews, and competitor datasets.
Developed a scalable full-stack system with an asynchronous FastAPI backend, PostgreSQL for structured business metrics, Redis caching, and a React dashboard with interactive analytics visualizations.

Achievements & Responsibilities

Design Team Member – ACM Student Chapter

Contributed to UI/UX design for technical events, social media campaigns, and event branding; applied design principles to create engaging and user-centric visuals.

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