As a recent graduate with a Master's in Computer Science from the University of Illinois Urbana-Champaign, I specialize in Generative AI research, focusing on Retrieval-Augmented Generation (RAG), particularly the ReAct RAG framework integrated with Agentic AI. Currently, I am preparing my first academic publication in these areas, driven by my passion for innovation and advancing AI capabilities.
In my role as a Software Engineer, I actively develop and optimize production-level ReAct RAG systems, train Machine Learning Models, and research novel Gen AI / ML methods. My responsibilities include fine-tuning system components, optimizing large language models, integrating breakthrough research insights, and ensuring highly accurate, contextually relevant responses in real-world applications to serve thousands of users daily.
I thrive at the intersection of cutting-edge research and practical implementation, continually learning and applying my expertise to tackle challenging problems and drive meaningful advancements in AI technology.
I am a continous learner and am always researching new technologies. I am currently researching reinforcement learning for data retrieval agents. I am actively on the hunt for AI / ML roles anywhere in the United States.
I am a continous learner and am always researching new technologies. I am currently learning NextJS and D3.JS. I am actively on the hunt for SWE / Quant internships anywhere in the United States.
I am highly supportive of startups and actively participate in open source projects as well. I am eager to collaborate with fresh ideas and individuals. If you have any topics you'd like to discuss, feel free to contact me via Mail
Deployed the first generative AI application in production at Country Financial, serving hundreds of agents
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Built an Agentic RAG system integrating multiple multimodal data sources to resolve complex agent queries in seconds instead of hours
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Trained a neural network document classification model to automatically process and index thousands of documents daily, enabling scalable AI-driven knowledge retrieval and automation across internal workflows
Migrated applications to Azure Web Apps using PHP & JavaScript, enhancing the EA and Cloud Platforms team's expertise in cloud deployment, monitoring, and logging
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Refactored and migrated actuarial research code from SAS to Python documented and transition process
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Enhanced skills in cloud infrastructure with a focus on Microsoft Azure, and honed expertise in CI/CD processes utilizing GitLab and pipelines
Contributed to a large-scale RAG research project (96% accuracy, 0.87 correlation) analyzing 5,800+ U.S. zoning codes to measure regulatory complexity and housing impacts
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Increased retrieval contextual accuracy by ~50pts through prompt chaining and reasoning, reduced p95 latency (~20ms)
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Applied LLM-based performance checks (RAGAS and Judge by LLM evaluators) to ensure high-quality research outputs
Led development of I-Heart, an innovative mobile app for congenital heart conditions using Flutter, Google Firebase, and 3D graphics libraries
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Created an interactive platform to improve communication and understanding around cardiac abnormalities
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Designed a user-friendly and accessible experience for patients and families, enabling physicians to better communicate cardiac conditions and treatment options
