Software Developer

• Jun 2023 - Present

Software Developer

• Sep 2020 - May 2023

● Started working on a complete eCommerce omnichannel suite from scratch. ● Implemented GraphQL APIs with modularity and high code reusability. ● Microservices Architecture for clients to just subscribe to certain services. ● Developed Event-based Architecture for maintaining modularity. ● Used Celery workers for queue management. ● Implemented code to integrate custom plugins and functionalities. ● Build automations using scripts and cron-jobs. ● Created highly responsive and scalable web applications for use in different industries and client environments. ● Single Handedly designed and implemented a Coupon Engine using Django to solve the limitations of Shopify Discount Vouchers. ● Lead a team of 4 developers to create a product facilitating a wallet and cashbacks program giving reasons to customers to make multiple purchases on D2C online platforms. ● Used by many D2C giants like BoAt, WowSkin, Safari bags, ITC Store, Plixlife. ● Ownership of end to end functional development and delivery along with automated unit and integration test cases. ● Conducted and managed regular team scrum, sprint planning and execution, closing feature discussion and requirements with PMs, mentoring peer developers to ensure timely delivery. ● Actively review PRs and provide constructive feedback for code improvements. ● Ensure to deliver high quality scalable solutions, clean and maintainable code with high performance. ● Focus on writing functional design docs & specifications for the implementation of features. ● Contributed to influencing and aligning the product vision by collaborating with customers, product management and internal partners. Show less

Data Science Summer Internship

• May 2019 - Jul 2019

• Prepared an LSTM model based on deep learning that detects the demand of inventories product • Used analytical skill sets in distributor inventory analysis, hospital data churning and pharmacy behavior analysis, thus optimizing Inventory, delivery model and minimizing expired products. • The smart AI based inventory management system would segregate products based on the demand of the market, thus decreasing stock in period and generating better revenues. • Prepared an LSTM model based on deep learning that detects the demand of inventories product • Used analytical skill sets in distributor inventory analysis, hospital data churning and pharmacy behavior analysis, thus optimizing Inventory, delivery model and minimizing expired products. • The smart AI based inventory management system would segregate products based on the demand of the market, thus decreasing stock in period and generating better revenues.

Credit Card Default Prediction & Analysis of Clients Dataset

• May 2019 - Jun 2019

The task is to predict whether a person is defaulted or not based on various personal and economic attributes. Various classifiers (Random forest, SVM, Logistic regression, Naive Bayes, K-neighbours) were trained to see how well they performed. The project is being implemented in python using libraries such as NumPy, matpoltlib, pandas, scikit learn. The task is to predict whether a person is defaulted or not based on various personal and economic attributes. Various classifiers (Random forest, SVM, Logistic regression, Naive Bayes, K-neighbours) were trained to see how well they performed. The project is being implemented in python using libraries such as NumPy, matpoltlib, pandas, scikit learn.

Object detection with YOLO (You Look Only Once)

• Apr 2019 - May 2019

The model is able to detect 80 different classes of objects in a photo frame using 5 anchor boxes after applying threshold filtering and non-max suppression filtering and the weights are taken from the official YOLO site. The model is able to detect 80 different classes of objects in a photo frame using 5 anchor boxes after applying threshold filtering and non-max suppression filtering and the weights are taken from the official YOLO site.

Diabetes patient prediction using KNN classification

• Mar 2019 - Mar 2019

The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset and the model performance analysis is done using Confusion Matrix, Classification Report, and Receiver Operating Characteristic (ROC) Curve. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset and the model performance analysis is done using Confusion Matrix, Classification Report, and Receiver Operating Characteristic (ROC) Curve.

Real-time Emotion Detection

• Feb 2019 - Feb 2019

Using fisher face classification and the dataset of the person in its different emotional states, the trained model will tell the emotional state in real-time. Example - If I have the image dataset of your face in different emotions, then by training in this model, I can detect your emotions in real-time. Using fisher face classification and the dataset of the person in its different emotional states, the trained model will tell the emotional state in real-time. Example - If I have the image dataset of your face in different emotions, then by training in this model, I can detect your emotions in real-time.