Aug 2024 – Oct 2024 · 2 mos · Noida, Uttar Pradesh, India

Jul 2023 – Aug 2024 · 1 yr 1 mo · Remote

• Worked as a Java Full Stack Developer implementing robust APIs to ensure optimal performance and user experience.
• Engineered a revamped Billing Statement interface, prioritizing an enhanced user experience to meet contemporary standards. Also worked on a major feature request to add customization options for clients. This transformation elevated efficiency and functionality, integrating an additional 30% of crucial information.
• Also, implemented the same design so as to change the existing one that is sent as a PDF attachment while emailing billing statements to patients.
• Developed the new print invoice screen for better user experience, executing end-to-end modernization from front-end to back-end, enhancing efficiency and functionality, along with including 20% new vital pieces of information.
• Worked on the Email Monthly statements tab of the software to revolutionize the web page for an unparalleled customer experience and hence, increase the feature adoption rate.
• Streamlined zHealth software’s Review Plus feature, transforming the cumbersome two-step review process into a seamless single-step flow through the implementation of a back-end flag system, reducing the review time by 50%.
• Skill Set: Java, Spring Boot, SQL, RestFul API, HTML, CSS, JavaScript

Jan 2023 – Jul 2023 · 6 mos · Remote

• Throughout the internship, I was engaged in diverse projects encompassing both front-end and back-end development, which had a huge impact on the company’s customers based in the USA.
• Developed SMS All and Email All functionality to help clients send SMS and emails to all patients in one click with the attached zHealth Pay link, which helped in the significant revenue growth of the company.
• Worked on SOAP Notes Customizations, which involved implementing new SOAP designs that catered to the individual requirements of each client.
• Skill Set: Java, Spring Boot, SQL, HTML, CSS, JavaScript, JSP

Jul 2021 – Sep 2021 · 2 mos · Noida, Uttar Pradesh, India

• Efficient Automated Glitch Receiver Output Peak (ROP) estimation:
• Developed a regression based Machine Learning model which was trained on different types of cells with different input wave forms to accurately predict the ROP(Receiver Output peak) value calculated by Cadence Tempus tool. The accuracy of the model was 95%. It will help to reduce the run time to calculate ROP significantly which was earlier very time consuming and exhaustive.

Jul 2020 – Sep 2020 · 2 mos

• Worked as a ML intern and built a Machine Learning model (with an accuracy of 97.67%) that will help a bank to identify the potential customers who have a higher probability of purchasing loan. This would increase the success ratio while at the same time reduce the cost of the campaign.
• Skillset: EDA, Logistic Regression, KNN Classifier, Naive Bayes, Random Forest and Decision Tree.