Kshitij Gaur

Software Engineer

Goa, IN.

About

A highly motivated and skilled software engineer with experience in full-stack web development, data science, and AI. Proficient in Python, React, and various other technologies. Passionate about building innovative solutions and contributing to impactful projects.

Work

Star Union Dai Ichi Life Insurance

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Software Engineer

Summary

Designed and implemented a full-stack web application to monitor policyholder transactions, integrating a Python ETL pipeline, SQL Server, and a React + Flask dashboard, reducing manual reporting and enabling near real-time insights. Delivered an automation tool for reconciliation uploads using Python and created a claim verification system by integrating UiPath processes, reducing manual effort by 70%, while ensuring high accuracy and faster operational turnaround.

Highlights

Designed and implemented a full-stack web application to monitor policyholder transactions, integrating a Python ETL pipeline, SQL Server, and a React + Flask dashboard, reducing manual reporting and enabling near real-time insights.

Delivered an automation tool for reconciliation uploads using Python and created a claim verification system by integrating UiPath processes, reducing manual effort by 70%, while ensuring high accuracy and faster operational turnaround.

JP Morgan Chase & Co.

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Product Data Science - Intern

Summary

Devised and programmed a credit spreading platform used by over 300 credit officers to efficiently analyze and distribute JP Morgan clients' financial data, improving collaboration, decision-making, and reporting accuracy across the organization. Revamped 3 Capital Structure tabs on iSPRESO, remodeled data mapping during vendor switch for last 10 year data on iSPRESO Engineered a spreading template from scratch for Broker-Dealers and onboarded the initial 10 clients onto the platform.

Highlights

Devised and programmed a credit spreading platform used by over 300 credit officers to efficiently analyze and distribute JP Morgan clients' financial data, improving collaboration, decision-making, and reporting accuracy across the organization.

Revamped 3 Capital Structure tabs on iSPRESO, remodeled data mapping during vendor switch for last 10 year data on iSPRESO

Engineered a spreading template from scratch for Broker-Dealers and onboarded the initial 10 clients onto the platform.

Education

BITS Pilani, India

B.E.(Hons) + Minor in Finance Computer Science

Awards

7th rank in Database Management Systems

Database Management Systems

Mentored 150 junior batch students as a Teaching Assistant.

KVPY Fellowship Stage 1

KVPY

Honored with the KVPY Fellowship Stage 1 in 2019(SA), NTSE 2018 rated among the top 2000 of more than 1 million candidates.

Skills

Programming

Python, C++, C#, Bash Scripting, Java, Golang, MERN Stack, MySQL, PostgreSQL, Git, REST APIs, Docker, Linux, AWS, Distributed Systems, CI/CD, Microservices, Kubernetes, System Design Principles, Generative AI, NLP, Computer Architecture.

Core Courses

Data Structures & Algorithms, Object Oriented Programming, Database Systems, Operating Systems, Computer Networks.

Maths & Finance

Derivatives & Risk Management, Securities & Portfolio Analysis, Machine Learning, Probability & Statistics.

Interests

Wall Street Club

Personal Finance, Investing, Taxes, Retirement Planning.

Projects

AI-Powered Mock Interview Coach

Summary

Built an AI-powered mock interview web app using Next.js, Firebase, and Vapi AI, enabling interactive, voice-based interview simulations to help users practice real-world interview scenarios. Developed a structured feedback system using Firebase and Zod validation, automating interview analysis and improving the accuracy of AI-driven feedback for candidate responses.

Artify

Summary

Built and deployed a full-stack AI image generator app integrating a responsive React.js and Tailwind CSS UI with a Node.js backend, enabling users to generate and share AI-created images along with a universal download functionality. Integrated OpenAl's DALL·E API for image generation and MongoDB for seamless data storage and retrieval, optimizing image load times by 40% through efficient backend routing, which enhanced overall app performance and user experience.

InsightAI

Summary

Built a GenAI-powered research assistant using Streamlit, FAISS, and GPT-3.5-turbo, applying advanced prompt engineering, semantic search, and vector embeddings to deliver accurate, real-time insights with relevance and real-world utility. Architected semantic embeddings, vector-based retrieval, and recursive text processing for accurate source retrieval and question-answering capabilities, enhancing the reliability and efficiency of the system.