I am a self-motivated and detail-oriented aspiring Data Scientist and AI Engineer with hands-on experience in developing end-to-end AI applications that solve real-world problems. I have worked on multiple projects involving Computer Vision, Retrieval-Augmented Generation (RAG), and Agentic AI systems, where I explored how intelligent agents and multimodal models can collaborate to perform complex tasks such as visual analysis, information retrieval, and automated decision-making. Through my internship experience, I gained practical exposure to building AI-driven solutions, strengthening my skills in Python, machine learning, deep learning frameworks, and AI system integration while working on structured data pipelines and real-world problem statements. I am particularly interested in Agentic AI architectures, intelligent automation, and scalable AI applications, and I enjoy experimenting with modern tools such as LangChain, LangGraph, and large language models to build innovative solutions. I continuously improve my technical expertise by developing practical AI projects and sharing them on GitHub, and I am currently seeking an entry-level opportunity in AI Engineering or Data Science where I can contribute to impactful projects, collaborate with experienced teams, and grow within the rapidly evolving AI ecosystem.
Specialized training in Artificial Intelligence, Machine Learning and Data Engineering with practical project experience.
Building analytical and quantitative reasoning skills through economic theory and statistical analysis.
Professional qualification in accounting, finance and business management.
Completed a hands-on internship focused on developing practical machine learning workflows and real-world AI solutions.
Engineered a multi-agent AI system for autonomous decision-making, combining ethical reasoning (utilitarian, deontology, virtue) with governance, strategy, and risk analysis for high-fidelity ethical decision simulations; aligned with ongoing research in agentic AI and AI safety. Integrated FAISS-based retrieval, Tavily, Serper, News API, and a risk calculator to enable context-aware, explainable, and real-time decision-making, improving transparency and robustness of AI-driven outputs.
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Built a full-stack sustainability prediction system using Ridge Regression and Logistic Regression to estimate circularity score and classify waste levels based on industrial production parameters.
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Manually annotated and trained a custom YOLOv8 Pose dataset (1100 frames) to accurately detect exercise keypoints and evaluate workout form in real time. Developed an AI-based exercise tracker and form evaluator using Flask + OpenCV, providing live rep counting, posture feedback, performance metrics, and audio coaching support.
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Built a production-style multimodal RAG system that generates structured Tokyo travel itineraries using semantic search and strict prompt grounding to reduce hallucination.
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Developed an AI-driven complaint resolution platform using Transformers, LSTM, and sentiment analysis. Automated complaint classification, root cause detection, and response generation via a Flask web app. Stored and tracked complaints using SQLite for scalable enterprise use.
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Designed an interactive dashboard to track sales, revenue, customer segments, and product performance. Used DAX, Power Query, and drill-through visuals to deliver actionable business insights.
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Created a Budget vs Actual dashboard using Excel/WPS with KPIs, pivot tables, and slicers. Analyzed variance and overspending trends across departments, regions, and categories.
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Built machine learning models to predict customer credit risk using financial and behavioral attributes, classifying customers as good or bad credit risks. Performed EDA, data preprocessing, and model evaluation using Gradient Boosting, Logistic Regression, and Gaussian Naive Bayes to support safer lending decisions.
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Built an OOP-based banking application in Python with account features and transaction operations. Integrated a Streamlit interface to enable interactive user operations.
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