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Full Stack Engineer with less than a year in Java, MERN, and AI/ML project development.
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Tejas Nikumbh is an aspiring Full Stack Developer with 4 months of internship experience in Java programming and business intelligence using Tableau. He has academic projects in full-stack web development (React.js, Spring Boot, ASP.NET Core) and AI/ML for sentiment analysis. Tejas is proficient in modern web technologies, databases, and machine learning concepts, eager to apply his skills in a dynamic development role.
Dr. Babasaheb Ambedkar Technological University
B.Tech · CSE
August 1, 2020 – June 30, 2024
KCE Society's M.J.College, Jalgaon
HSC
June 1, 2019 – May 31, 2020
Pg-DAC CDAC(Kharghar) Mumbai.
Pg-DAC
N/A – June 30, 2025
Mindlogics Business Intelligence Lip
Tableau
September 1, 2022 – October 31, 2022
Jalgaon, Maharashtra, India
R3SystemsIndia Private Limited
Java Programming
December 1, 2021 – January 31, 2022
Nashik, Maharashtra, India
Vidya Al Study Companion
June 28, 2026 – January 1, 2025
Vidya AI Study Companion is an interactive learning platform powered by React.js on the frontend, and a hybrid backend architecture utilizing both Spring Boot and ASP.NET (.NET). It allows students to upload study materials, such as PDFs and notes, and access AI enhanced features like intelligent search, and contextual Q&A assistance. Key project highlights include React Hooks, Spring Boot REST controllers, ASP.NET Core integration, PDF ingestion and user analytics dashboards.
Invoice Processing System
June 28, 2026 – January 1, 2025
An Invoice Processing System built with React.js frontend and ASP.NET Core (.NET) backend, offering full lifecycle invoice management create, view, edit, delete invoices with vendor, tax, totals, and date fields. Features include invoice search, sorting, filtering, server-side PDF export, role-based access control, audit logging, and a dashboard with invoice analytics. This project demonstrates strong capabilities in React.js, ASP.NET Core, REST APIs, CRUD operations, PDF generation, relational database design, and enterprise-grade web development.
Sentiment Analysis Aiding Depression using Machine Learning
June 28, 2026 – January 1, 2024
A Python-powered sentiment analysis system to aid depression detection by classifying user-generated text for emotional cues. Key features include explainable predictions via feature importance or attention mechanisms, enabling ethical usage in mental health support contexts. This project reflects skills in Python, Machine Learning, NLP, Sentiment Analysis, BERT embeddings, SVM, Random Forest, Model Evaluation, Feature Engineering, and Depression Detection.
Cultural Fit Analysis
The candidate's academic projects cover a range of technologies (React, Spring Boot, ASP.NET Core, Python ML) and domains (AI study companion, invoice processing, sentiment analysis), suggesting adaptability and a broad interest in technology. This diversity could indicate a good cultural fit for a dynamic environment. However, the lack of non-academic or team-based project details limits a deeper assessment of collaboration and initiative.
Soft Skills & Operational Fit
The candidate's project descriptions suggest an ability to work on diverse technical challenges, from web development to machine learning. The academic nature of projects indicates a learning-oriented individual. However, without direct work experience beyond internships, it's difficult to assess operational fit and soft skills like teamwork or problem-solving under pressure.