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TEJAS GAIKWAD

Software Engineer

About TEJAS

Results-driven Software Engineer with a strong foundation in computer science and hands-on experience in frontend and fullstack web development. Proficient in building scalable, responsive applications using Angular and the MERN stack, with exposure to machine learning and NLP-based solutions. Adept at collaborating in agile teams, integrating RESTful APIs, and delivering high-quality, user-centric software solutions.


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I enjoy solving complex problems and actively practice Data Structures and Algorithms, having solved 1000+ problems on LeetCode with a Knight-level contest rating. I’m eager to learn, adapt quickly, and contribute to impactful software solutions in a collaborative environment.

Skills

  • Angular
  • Data Structures & Algorithms
  • Express.js
  • Git
  • Github
  • Gitlab
  • java
  • JavaScript
  • Linux
  • Machine Learning Fundamentals
  • MERN Stack
  • MongoDB
  • MySql
  • Neural Networks
  • Node.js
  • NoSql
  • Postman
  • Python
  • React.js
  • Responsive Web Design
  • Tensorflow
  • Typescript

Work Experience

06/2025-12/2025
Trainee Software Engineer
Neebal Technologies

Frontend Development (Angular)

•Developed responsive frontend modules using Angular for enterprise-level management systems.

•Implemented User Management features including user creation, role assignment, and access control.

•Built domain-specific modules such as Airplane/Airline Management with dynamic forms and data handling.

•Integrated frontend components with backend APIs using HTTP services and RESTful endpoints.

•Used GitLab for source code management, version control, and collaborative development workflows. 

06/2024-09/2024
Internship Trainee
Ion Exchange (India) Ltd.

Chatbot Development

•Built an intent classification chatbot using Python, TensorFlow, and Neural Networks for automated user interaction.

•Designed and maintained an intents.json dataset containing training patterns, intent labels, and response mappings.

•Applied NLP preprocessing techniques such as tokenization, stemming, and vectorization to improve model performance.

•Trained and evaluated a neural network model to accurately classify user queries into predefined intents. 

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