I am a final-year Computer Science graduate, expected to complete my degree in 2026, with hands-on experience building AI-powered systems such as NLP classifiers, RAG pipelines, and deployed machine learning applications. I am skilled at owning problems end-to-end: identifying friction points, prototyping solutions, validating with data, and documenting for reuse. I am fluent in working with large language models (LLMs), Python automation, and AI workflow design.
Throughout my academic career, I have developed strong technical skills in programming languages including Python, Java, JavaScript, SQL, and C. I have applied these skills in various AI and machine learning projects, focusing on NLP, embeddings, fine-tuning models, and building pipelines that balance accuracy, explainability, and computational efficiency.
I have successfully scoped and built an NLP classification system from scratch to detect political bias in news articles, managing the entire pipeline from problem definition to model deployment and documentation. Additionally, I engineered an end-to-end ML pipeline for PCA dimensionality reduction, deploying it as an interactive web application to make complex data science accessible to non-technical users.
My education at Sreenidhi Institute of Science and Technology has provided me with a solid foundation in artificial intelligence, machine learning, deep learning, NLP, LLMs, generative AI, and database management systems. I have also actively participated in certifications and volunteer activities, including data structures and algorithms, SQL, and organizing community health and awareness programs.
I am passionate about leveraging AI and machine learning to solve real-world problems and am eager to contribute my skills and knowledge to innovative projects and teams. I am a proactive learner and collaborator, committed to continuous improvement and delivering impactful solutions.
GPA: 8.24 / 10; Relevant coursework: Artificial Intelligence, Machine Learning, Deep Learning, NLP, LLMs, Generative AI, DBMS
Scored 92.5%
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