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About me


  • 2022 - 2024
    University at Buffalo, The State University of New York, United States

    Master of Professional Studies: Data Science

    Course work: Python, Data Structures, Algorithms, Data Manipulation Query Language, Machine Learning

  • 2022 - 2024
    Vellore Institute of Technology- Chennai, India

    Master of Technology: Software Engineering

    Course work: Java, Python, C/C++, Database Management, Operating Systems, Object-Oriented Analysis and Design, System Design, NLP, Computer networks


  • 2020 - 2022
    Quinbay Technologies.

    Full stack Developer

    • Led the end-to-end development of web applications, leveraging Java, Python, React JS and REST APIs to deliver
    robust and scalable solutions.
    • Implemented Spring, and Hibernate frameworks to create backend systems that efficiently processed and managed
    large volumes of data.
    • Developed and deployed scalable web applications utilizing SOA and micro-services design patterns, resulting in a
    30% increase in application performance and seamless integration across multiple platforms.
    • Collaborated with cross-functional teams to gather and analyze requirements and implement multi-tiered web
    applications, resulting in a 20% reduction in development time and an increase in overall product quality.
    • Collaborated with open-source frameworks and utilized APIs like Junit to optimize development.
    • Skillfully administered both relational databases, such as SQL, and NoSQL databases like MongoDB, ensuring
    streamlined data storage and retrieval processes.
    • Utilized agile methodology and JIRA software to lead the design and development of a new feature, resulting in a 15%
    increase in customer satisfaction ratings.
    • Conducted knowledge-transfer sessions and workshops to disseminate best practices and emerging technologies,
    contributing to the team\’s technical skill development.

  • 2019 - 2019
    Triculin Technologies Pvt. Ltd.

    Software Engineer Intern

    • Developed a Python-based interface for disease prediction using machine learning algorithms. The interface was able to
    achieve an estimated 85% accuracy rate, which is higher than previous methods.
    • Analyzed the disease prediction problem by inputting various symptoms into the model and detecting the presence of
    the disease. This involved cleaning and preprocessing the data, selecting the appropriate machine learning algorithms,
    and tuning the hyperparameters.
    • Collaborated with team members to improve the overall interface usability and functionality. This included adding
    features such as a user guide, a feedback form, and a help desk.
    • Received positive feedback from end-users and saw an increase in the adoption of the tool. This shows that the interface
    is user-friendly and effective in predicting diseases.