About me
A snapshot of who I am and what drives me
Building the future with Machine Learning, Deep Learning, and AI-powered innovation.
Hello! I’m a final-year B.Tech student at PES Modern College of Engineering, specializing in Machine Learning, Deep Learning, and Natural Language Processing (NLP). I have a strong foundation in AI and have applied my knowledge to build intelligent, data-driven solutions through both academic and personal projects.
In addition to core AI skills, I have hands-on experience with LangChain, where I design applications powered by large language models (LLMs), and FastAPI, a modern, high-performance web framework for building robust and scalable APIs. These tools have helped me develop real-world AI systems that are production-ready and optimized for deployment.
I’m passionate about exploring the intersection of AI and software engineering—building models that understand language, solve problems, and offer real value to users. I’m also constantly expanding my technical stack and keeping up with the latest advancements in the AI ecosystem.
Let’s connect and create something meaningful with AI!
What I do
Data Analyst
I analyze, clean, and visualize data to uncover insights, drive decisions, and build predictive models using tools like Python, SQL, Tableau, and Power BI for data-driven solutions.
Machine Learning Engineer
I develop and deploy machine learning models, preprocess data, and optimize algorithms using tools like Python, TensorFlow, and scikit-learn to create intelligent, data-driven applications and solutions
Data Engineer
I design, build, and maintain scalable data pipelines and architectures, ensuring efficient data flow, storage, and accessibility using tools like SQL, Python, Spark, and cloud platforms for analytics.
My Experience
Machine Learning Intern
I completed my internship at NV Analytical Solution, where I gained hands-on experience in data visualization and AI application development. I worked on building interactive dashboards in Looker Studio, using HCES big data to analyze and present key insights effectively. This involved data cleaning, structuring, and creating visual reports for better decision-making. In the later phase of my internship, I developed an RAG-based chatbot using LangChain, integrating retrieval-augmented generation techniques to provide accurate, context-aware responses. This project enhanced my understanding of LLMs, vector databases, and prompt engineering, bridging the gap between data analytics and conversational AI.
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