Built an end-to-end Retrieval-Augmented Generation chatbot using LangChain and the OpenAI API. Implemented document ingestion, chunking, embedding generation, FAISS vector indexing, semantic search, a critic/validation loop, prompt and retrieval iteration, modular components, and a Streamlit interface.
1roles
36skills
1education
1credentials
About
Early-career AI/ML engineer with hands-on Python experience across the machine learning lifecycle, including data preprocessing, model development, evaluation, deployment, and Generative AI applications.
Experience
AI/ML Intern
Edunet Foundation | AICTE | IBM SkillsBuild
Dec 2025 – Jan 2026 · Remote
Completed a 6-week applied AI/ML internship, writing Python-based workflows across the ML lifecycle from data ingestion through model evaluation. Performed data preprocessing and feature engineering on structured datasets, developed and evaluated machine learning models, and debugged data quality issues.
Education
Saveetha Engineering College
B.Tech, Artificial Intelligence and Machine Learning
May 2026
CGPA: 7.8
Skills
PythonSQLPandasNumPyGitGitHubModular and reusable code designCode documentationDebugging and troubleshootingMLflowScikit-learnXGBoostPyTorchClassificationRegressionClusteringFeature engineeringHyperparameter tuningGridSearchCVModel evaluationLLMsGenerative AIRAGHugging Face TransformersPrompt engineeringEmbeddingsSemantic searchLLM evaluationLangChainLangGraphOpenAI APIFAISSVector databasesFastAPIDockerStreamlit
Projects
Employee Attrition Prediction Using Machine Learning
Built an end-to-end employee attrition classification pipeline using Logistic Regression, Decision Tree, Random Forest, and XGBoost. Performed preprocessing, feature engineering, label encoding, EDA, GridSearchCV tuning, evaluation with Accuracy, Precision, Recall, F1-Score, and ROC-AUC, feature-importance analysis, and Streamlit deployment.