I began my career at Oracle in July 2023 as a Technical Analyst, joining the organization as a fresher graduate. My onboarding experience included a comprehensive three-month program focused on compliance, domain fundamentals, and preliminary technical training, all designed to prepare me for real-time project responsibilities.

Following the training period, I was paired with an experienced team member through Oracle’s buddy program, which played a key role in helping me navigate my day-to-day responsibilities. This structured support system enabled me to quickly develop a strong understanding of my team’s operations and the product ecosystem I would be contributing to.

As I grew more confident and gained end-to-end visibility into our product workflows, I identified opportunities to enhance efficiency by reducing manual effort through automation. Leveraging my solid programming background and project experience from college, I began developing automation solutions tailored to our team’s needs. To date, I have successfully delivered 5–6 automation projects, each contributing to measurable productivity improvements.

In addition to my primary responsibilities, I am also collaborating with a dedicated cross-functional team focused on building and maintaining an AI-driven application aimed at further optimizing operational processes. This initiative is expected to significantly reduce repetitive tasks and enable engineers to focus on high-impact work.

My journey at Oracle so far has been both enriching and growth-driven, and I look forward to continuing to contribute meaningfully while building a strong and fulfilling career within the organization.

My Experience at Oracle

• Automations

• APEX Applications

• AI use cases (Agentic and MCP)

• Browser extensions

• Internal tools for productivity

A Hybrid Extractive-Abstractive Framework With Pre & Post-Processing Techniques

Jan 2023 - Jun 2023Jan

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The goal of this paper is to enhance text summarization using a hybrid methodology. The process of producing a condensed version of a text while keeping its essential details is known as text summarization. In this study, we have presented a method for training the T5 model on the SAMSum dataset with conversation sentences to increase its effectiveness in text summarization. To determine the impact of training the T5 model on the dataset, the model is assessed using the ROUGE metric both before and after training. ROUGE is a set of metrics used to evaluate the quality of automatic summaries by comparing them to reference summaries based on the overlap of n-grams, word sequences, and other linguistic units. To enhance the quality of the generated summary, our hybrid approach makes use of extractive and abstractive summarization techniques as well as pre-and post-processing techniques.There is an improvement in the ROUGE metrics of the model before and after training. ROUGE1 before training was observed to be 25.53 while ROUGE1 after training is calculated to be 45.17.

Face Recognition Based Attendance System

Dec 2021 - Feb 2022

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The system's goal is to make the attendance marking process quick and easy because the teacher's mundane job in class is time consuming in monitoring the students while marking attendance and ensuring that no proxy attendance is marked. To solve the problem efficiently, the system employs a machine approach that makes use of Python's OpenCV library. The Haar cascade algorithm and the LBPH algorithm are used for face detection and recognition. The system is designed to be s ufficiently accurate in comparison to oth