AI & ML Graduate
AI & ML graduate with hands-on experience in Machine Learning, Deep Learning, Natural Language Processing, Generative AI, backend development, predictive modeling, RAG systems, REST APIs, and AI-powered applications.
KodNest Technologies
Mar 2025 – Dec 2025
Developed data-driven applications using Java, React.js, Node.js, and MySQL; designed RESTful APIs and optimized database queries for scalable systems; worked with structured datasets and backend systems supporting business applications.
Bluestock
Aug 2025 – Sep 2025
Implemented scalable backend modules following clean architecture principles; optimized API workflows and improved system maintainability; enhanced backend reliability through structured development practices.
Awign
Mar 2026 – May 2026
Annotated and validated datasets for AI and Machine Learning applications; maintained data quality and consistency for model training workflows; conducted quality assurance checks on large-scale datasets; supported dataset preparation processes used in AI systems.
Bachelor of Technology, Artificial Intelligence and Machine Learning
2021 – 2025
CGPA: 9.04/10
Developed fraud detection models using Logistic Regression, Random Forest, and ANN; performed feature engineering, preprocessing, and imbalance handling; evaluated models using Precision, Recall, F1-Score, and Accuracy; built a Streamlit application for real-time fraud prediction and monitoring. Technologies: Python, Scikit-learn, TensorFlow.
Built a Retrieval-Augmented Generation pipeline for document-based question answering; implemented semantic search using embeddings stored in FAISS vector databases; integrated OpenAI APIs through LangChain; designed scalable document retrieval and response workflows; developed an interactive document querying interface. Technologies: Python, OpenAI API, LangChain, FAISS.
Developed a full-stack application with JWT-based authentication and authorization; designed scalable REST APIs and optimized MongoDB queries through indexing; implemented modular client-server architecture. Technologies: MongoDB, Express.js, React.js, Node.js.