Nitesh Dubey

Hi, I'm Nitesh, welcome to my website. I'm working as a Member of Technical Staff at DevRev Inc. I'm an avid learner and have worked with Fullstack Development, Mobile Development and Machine Learning. I'm a 2022 Computer Science grad From Indian Institute of Information Technology Guwahati.

My Resume

Experience

Member of Technical Staff

DevRev Inc.- Dec'21 to present

Developed UI of Issue and Ticket Management Quadrant of DevRev, in a team of 8 people.
Fix and improve End to End test and unit test coverage in DevRev web app
Implemented Product Analytics inside DevRev, with Segment and Heap Analytics to facilitate product decisions, and usage analysis
Collaborated with UX and Backend team, in the development of various new feature and bug fixes in the webapp

Software Engineer Intern

Legato Health Technologies ( Anthem Inc. )- May'21 to July'21

Anthem sends important emails to a large number of their customers, but some of those emails don't get delivered, so they ring the call centres, and increase load.
So, I was tasked to make a System which can track and store the emails in a database, which bounce back and don't get delivered to the recipients.
I developed the backend of the system using AWS SES, AWS SNS, AWS SQS, AWS Lambda and MongoDB
I also developed an Email client GUI for sending email, and receiving bounce emails using Nodejs, Reactjs and MaterialUI

Software Engineer Intern

Wonderquill Business Solutions- May'20 to July'20

Worked on Developing the Frontend of a web platform where users can write and share their stories, tales and poems.
Added a Markdown editor functionality to the website, where the users can write text, and implemented autosave feature using HTML5 localstorage
Used GraphQL APIs to sync the user data to the server.

SKILLS

Frontend

Backend

Machine Learning

Others

Projects

Splash - Image Search

Splash is a fully responsive Image Search Website, where users can search for images by entering related tags in the search bar. They can log into the website and can also upload their own images to the website, which will automatically be tagged. The Image Autotagging will be handled by my ML model.

If the user uploads images to the website, it will run this model on the image. The model will predict and rank both seen and unseen tags (not limited to training set) on the image. It’ll keep increasing its tags vocabulary with the user suggested tags. I built this model by implementing Fast Zero shot Image Tagging research paper.

Tech stack used : Nextjs, Nodejs, Chakra-ui, MongoDB, Keras, Tensorflow, Python

Code Website Machine Learning Model

Intown - Events Near You

It is a mobile app made using react native, which shows local events happening around user's location. The users can become event hosts and create new events or can register, like and attend the events around them.

Tech stack used : React Native, React Navigation, React Native Elements, Firestore, Firebase Auth, Geolocation Service.

Code and Demo

Vient - Tiktok UI

It is the frontend of a social media app (like tiktok) built with React Native, where users have their own profiles, they can record and share short videos with the community, follow people, chat with them, search the app for more videos and much more

Tech stack used : React Native, React Native Element, React Navigation, Expo.

Code and Demo

Property Management

It is the university DBMS group Project in which a website was made for a real estate company to sell / rent properties, with interfaces for both agents and their manager. I worked on making the interface of the agent, where he can sell or rent the property to the customer.

Tech stack used : MYSQL, Nodejs, HTML, CSS, Javascript, Python.

Code

Maze Game

It is a game I built, using Reactjs and Hunt and Kill Maze Generation Algorithm. In the game, the player has to collect all the points in the n x n Maze in shortest time possible.

Tech stack used : Javascript, Reactjs.

Code Demo

Image Captioning

Its a Deep Learning network, trained on Flickr 8k dataset to predict suitable image captions for an input image. Merge architecture was used for training image captioning network and beam search decoder was used for inference. I had used Pre-trained weights of Inception model and Glove 200d word embedding to extract image features from the input image and word relations from the text captions respectively.

Tech stack used : Python, Keras, Deep Learning

Code and Demo

Text Summarization

It is a Text Summarization network trained on Amazon Fine Food Reviews Dataset, which predicts suitable summary for the reviews of customers on food products. The network was trained using seq2seq architecture, with BI-LSTM encoder and LSTM decoder along with Attention Mechanism..

Tech stack used : Python, Keras, Deep Learning

Code and Demo

Fitnessbook

Its a simple fitness app, made in a Hackathon, which contains step detector/Pedometer, keeps record of distance run by the user, and provides health related articles.

Tech stack used : Java, Android Studio

Code

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