As a Data Engineer on the Anti-Money Laundering (AML) team at Citi Bank, I am responsible for designing, building, and maintaining data pipelines. I work with large datasets to extract, transform, and load data into our systems, ensuring data quality and integrity. I am also involved in optimizing data workflows and implementing new technologies to improve our data infrastructure.

Accepted into the prestigious Headstarter AI Software Engineering Fellowship. Over the course of 7 weeks, I am engaged in creating 5 innovative AI applications and participating in weekly hackathons. This fellowship provides an opportunity to expand and refine my portfolio while collaborating with engineers from top companies such as Google, Apple, Bloomberg, Citadel, Two Sigma, Meta, and other leading startups. Through this intensive program, I am enhancing my software engineering skills and gaining valuable experience in AI application development.

Collaborating with Professor Parteek Kumar on finetuning a LLama 2 model for FarmingGPT, an innovative chatbot designed to assist farmers. The chatbot is capable of processing input and delivering output in Hindi. Responsibilities include data preprocessing, model training, and performance evaluation. Successfully managed and processed over 100,000 data entries to enhance model accuracy. Leveraged skills in Python, TensorFlow, and natural language processing to improve the chatbot's functionality and user experience. This role emphasizes practical application of machine learning techniques in a real-world agricultural context, aiming to provide accessible AI-driven solutions for the farming community.

Currently collaborating with Rosie Mueller, Assistant Professor of Economics at Whitman College, on a research project examining the impact of heat on productivity. Leveraging R-Studio and Python, I am responsible for data engineering tasks, including the integration and cleaning of population and weather datasets. My role involves organizing and preparing data for analysis to support meaningful insights and conclusions about the relationship between heat and productivity.

Worked on the development and implementation of a Machine Learning model for the Adult Census Income Prediction Project, predicting annual income levels (>50K/<=50K) using Python. Addressed key challenges in data exploration, cleaning, and feature engineering to enhance model accuracy. Successfully deployed the machine learning model on AWS, optimizing system architecture to reduce model response times by 40% and improve overall performance. Conducted comprehensive testing and implemented robust logging mechanisms to monitor system behavior and performance. Skills utilized: Python, Machine Learning Algorithms, NumPy, Pandas, AWS, Data Exploration, and Cleaning..

As a Virtual Cybersecurity Intern at SAP, I gained valuable experience in one of the world's leading enterprise software companies. I collaborated with a team of experienced cybersecurity professionals and worked on various projects, including conducting vulnerability assessments and implementing cybersecurity best practices. This role allowed me to stay abreast of the latest trends and technologies in cybersecurity and provided hands-on experience in a professional setting. The internship helped me develop a strong foundation in cybersecurity and reinforced my passion for the field.

As a Remote Extern at PwC, I applied various quantitative and qualitative data analysis techniques to draw reasonable conclusions and support strategic decisions. I learned about the vision, mission, and operations of CHAT, as well as key concepts in the nonprofit sector, with a focus on generational giving trends, particularly among Gen Z. My role involved identifying notable patterns and deriving insights to aid strategy development. Additionally, I utilized online lead research tools to source authentic contact information for target leads. This experience enhanced my skills in trends analysis, market analysis, and data-driven decision-making.