Teaching Assistant | Researcher | Data Scientist
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>> Exploring LLM, Neural Nets, and Diffusion models
>> HackThisFall 4.0 Best of ALL Girls Category Winner
>> Associate Data Scientist at Casepoint Pvt. Ltd.
>> Fellow at DO School by Mercedes-Benz received a grant.
>> Building tool at BuildSpace S5
>> served as mentor at Hack This Fall 2024 virtual Hackathon
>> worked on the Initiative of Project Green - leveraging technology for reforestation
>> Been a mentor as an AI/ML Expert at CreateHerFest Hackathon
>> Contributor - Harvard & University of Toronto Global Study on Social Entrepreneurship
>> got into Residency Delta 2025
I am an AI Engineer and Systems Builder with hands-on experience designing, deploying, and scaling end-to-end AI solutions. My work focuses on building production-ready AI systems using LLMs, FastAPI, structured data extraction pipelines, workflow automation tools, and cloud-based deployments.
I have developed AI-powered applications that convert unstructured data into structured outputs, built scalable APIs, implemented RAG pipelines, and integrated automation tools into real-world workflows. Beyond technical implementation, I have led and mentored teams at global hackathons, translated complex AI systems for non-technical stakeholders, and created clear documentation and SOPs for maintainability and scalability.
I operate at the intersection of AI, product thinking, and system ownership - ensuring reliability, performance, and continuous improvement of deployed solutions. I am particularly interested in applying AI automation to operational environments where efficiency, scalability, and measurable impact are critical.
I am currently pursuing a master's degree in Computer Science, with a focus on Artificial Intelligence, Machine Learning, and software systems. My academic training includes data structures and algorithms, database systems, cloud computing, and full-stack development.
In addition to coursework, I have applied my education through hands-on AI projects, including building LLM-based systems, automation pipelines, and production-ready APIs. My learning has been strongly project-driven, combining theoretical foundations with real-world implementation in AI, system architecture, and scalable application development.
