Avijeet Nair Hi, I'm Avijeet.

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SKILLS - Java, JavaScript, TypeScript

- React, Redux, Saga, RTK-Query, Tailwind, Material UI

- NodeJS, Express, SQL, MongoDB, GraphQL

- AWS
EXPERIENCE - Wipro Limited(Client: BT)
 Senior Project Engineer
 (Jul 2024 - Present)

- Wipro Limited(Client: BT)
 Project Engineer
 (Apr 2023 - Jun 2024)

- Wipro Limited
 Project Engineer (Trainee)
 (Apr 2022 - Mar 2023)

Senior Project Engineer - Worked on two projects - Cloud Portal UI and Wifi-Hub GUI

- Developed a scalable, widget-based UI to manage 1.5 million devices

- Led the Device Group design/development cycle,

- Reduced deployment time by 75% .

(contd.) - Improved performance by 40%

- Reduced cloud-costs by 15%

- Implemented EN 18031 web-security compliance

- ErP compliance for energy-efficiency

Project Engineer - Developed Hub-Management-GUI for a first-to-market Wifi-7 device, Smart-Hub 4

- Improved Accessibility by 65% as per WCAG 2.1 guidelines.

- Made GUI fully-responsive, across devices.
(contd.) - Integrated Wifi-7 to GUI, while ensuring backward compatibility with legacy devices.

- Designed Figma UI/UX mockups from requirements

- Recognition for innovative hackathon solutions in AI & Alexa-skills.
Project Engineer - Trained under Java + MERN + AWS Full-Stack Program

- Finished in the top 1% of a cohort of 350.

- Built two end-to-end full-stack projects.
EDUCATION - B.Tech in Information Technology

 (Maulana Abul Kalam Azad University of Technology, formerly WBUT)
 (Aug, 2018 - Jun, 2022)

CGPA: 8.98
PROJECTS CRM Application

- Designed an MVC-based system with role-based access (Customer, Engineer, Admin)

- Integrated asynchronous email notification service

- Deployed on Google Cloud Compute Engine using Nginx
PROJECTS Movie Booking Application

- Users can book movie tickets, theatre-owners can create/update show-times.

- Integrated an Email-Notification-Service for payments confirmation.

- Deployed on AWS via Amazon EC2 instance
PROJECTS Brain Tumor Detection and Segmentation

- Trained a CNN model on a dataset of 2000 MRI images.

- Detection accuracy - 93.5% , Segmentation accuracy - 99.67% , F1 score - 0.83
CERTIFICATIONS
- Machine Learning - Stanford University



- Full Stack Development - Relevel by Unacademy
mario bros