Tahsin Rahman Chowdhury

NIKE ECOMMERCE STORE CLONE:

• Designed and built a Nike-style eCommerce platform using Next.js, TypeScript, Tailwind CSS, Prisma & Drizzle ORM, and Neon PostgreSQL. Integrated BetterAuth for secure user authentication and Stripe for payment processing.
• Normalized the product database to handle variations in size, color, and price, enabling accurate inventory and flexible pricing.
• Implemented dynamic product listing, detail pages, cart, and checkout features; leveraged Zustand for state management to achieve
• Used Devin AI to generate dynamic product content and descriptions, improving user engagement and discoverability.
  

AI RESUME ANALYZER:

• Implemented natural language processing to extract skills and experience, compared them against job descriptions, and provided feedback on skill gaps.
• Developed an AI-driven resume analyzer with a React front-end and Flask/Python back-end. Enabled job seekers to upload resumes for parsing and scoring.
• Stored uploaded resumes and parsed data securely on AWS S3 and RDS; deployed APIs on EC2 for scalable processing.

Netflix Clone:

• Created a Netflix-like streaming web application using Next.js and React, integrating TMDB APIs for movie and TV data.
• Implemented user authentication, search functionality, dynamic content pages, skeleton loading, with the ability to show trending movies based on user search/selection.

AI SaaS Platform:

• Built a multi-tool AI software-as-a-service platform using Next.js and TypeScript, integrating multiple AI services including chat, code generation, and summarization.
• Developed a credit-based subscription system with Stripe for payments, managed user roles and usage limits, and stored data in a Postgres database via Prisma.
• Deployed the platform on AWS/Vercel for scalability and used server actions to optimize concurrency and performance.

Additional:

• Reviewed code and debugged issues for peers, collaborating with professors to enforce clean coding standards and efficient algorithms.

• Created comprehensive test cases for Java, Python, React, and SQL projects, ensuring edge-case coverage and reducing production bugs.

• Collaborated with back-end developers to design secure APIs and with front-end teams to implement them using Axios in React, resulting in dynamic routing and a 99% reduction in load times.

• Developed and maintained databases, servers, and web applications; supervised junior developers and designers, assigned tasks, and monitored project performance.

Exploratory Data Analysis on Crypto and Football:
Performed detailed exploratory data analysis using Python (Pandas, Matplotlib, Seaborn) to uncover player performance patterns and cryptocurrency price trends.

• Cleaned and visualized large datasets to identify correlations and performance drivers.
• Delivered actionable insights through Power BI and Tableau dashboards to highlight volatility and trends.

Iris Classification & Boston Housing Regression:
Developed machine learning models for structured data classification and regression problems using Scikit-learn.

• Achieved high model accuracy through feature scaling and hyperparameter tuning.
• Visualized model performance metrics in Power BI for stakeholder interpretation.

SQL Data Exploration & ETL Pipelines:
Executed complex SQL queries for data exploration and automated ETL pipelines for cleaning and transformation.

• Streamlined workflows by using SQL joins, subqueries, and CTEs to merge multi-source data.
• Deployed ETL processes on AWS EC2 and automated cleaning tasks using Python and Pandas.

Tableau Visualization & Reporting:
Designed interactive Tableau dashboards for KPI tracking and business performance visualization.

• Built data stories combining SQL-extracted data with visual analytics, improving executive decision-making.
• Enhanced reporting speed and clarity by automating refresh cycles through SQL-based data pipelines.

Data Cleaning & Feature Engineering with Python:
Utilized Python libraries (Pandas, NumPy) for handling missing values, outlier detection, and data normalization.

• Improved downstream ML model accuracy by ~15% through engineered features and robust preprocessing pipelines.
• Packaged cleaned datasets into analytics-ready tables for use in Power BI and machine learning workflows.

• E-commerce business with Import/Export and Wholesale Distribution, using Digital Marketing and Fulfillment by Amazon.
• B2B Sales, Business Management, Project Management, Book Keeping, Accounting, Financial services.