About
I’m a full‑stack software engineer who thrives on solving complex problems with clean, maintainable code. Since starting as an intern at Squareboat Solutions in early 2022 and converting to full‑time later that year, I’ve built robust Node.js microservices, optimized Redis‑powered caches, and designed data pipelines that power real‑time healthcare and analytics apps. My passion lies at the intersection of backend architecture and data insights — I’ve architected ETL workflows, created interactive dashboards in Python and Tableau, and led a Swiggy case study that delivered actionable business intelligence. I care deeply about performance, scalability, and user experience: every API I write is built for reliability, speed, and clear documentation. Outside of code, I’m constantly experimenting with open‑source tools, fine‑tuning SQL queries for fun, and exploring new ML libraries. If you’re looking for someone who blends technical rigor with creative problem‑solving to build impactful products, let’s connect and make something great happen.
Experience
Software Engineer @ Squareboat Solutions
July 2022 – March 2023
Build and maintain critical components used across various platforms with a focus on accessibility, performance, and robust design.
Software Engineer Intern (Internship) @ Squareboat Solutions
February 2022 – June 2022
Developed scalable UI solutions, led small teams, and integrated with back‑end APIs.
Projects
LLM-Knowledge-System
A generic LLM knowledge system that integrates documents, web search, and multiple LLM providers with grounded, source-aware answers.
Myntra Online Retail Customer Segmentation
Led a data‑driven customer segmentation project for Myntra Gifts Ltd., leveraging 541,909 online retail transactions (Dec 2009–Dec 2011) to uncover three distinct customer cohorts through RFM feature engineering and K‑Means clustering. By cleaning, transforming, and visualizing the data in Python, I identified a high‑value segment driving ~60% of total revenue, a moderate group ripe for upsell, and an at‑risk cohort requiring reactivation. The insights generated actionable marketing and inventory recommendations—enhancing retention, optimizing pricing, and reducing churn—resulting in a clear roadmap for data‑backed decision‑making.
Recommendation System | Movies & Anime
Developed a hybrid recommendation system for movies and anime that combines TF‑IDF content‑based filtering with K‑Means clustering to deliver highly personalized top‑5 recommendations. Built end‑to‑end in Python—leveraging Pandas, Scikit‑Learn, PCA for visualization, and silhouette analysis to determine optimal clusters—resulting in clearly defined content segments (Silhouette score ≈ 0.43) and actionable insights for targeted user engagement.
Contact
Interested in working together? Feel free to send me an email! inbox.rakshitpandey@gmail.com