AI / ML Engineer · Final Year
Building LLM systems, NLP pipelines, and practical GenAI applications.
I’m a BSc Computer Science student in Pune focused on real-world AI engineering across LLMs, RAG, NLP, and Python-based systems.
Selected work
Compared baseline, L1, and L2 Logistic Regression models for employee attrition on 1,350
records. L2 Ridge achieved 88.5% accuracy and 0.958 ROC-AUC. Dropped
Annual_Bonus_Squared after detecting multicollinearity (R² = 0.97).
Python · scikit-learn · Pandas · Seaborn · L1/L2 Regularization · ROC-AUC
Built a scikit-learn pipeline with ColumnTransformer and GridSearchCV to evaluate 5 regression models on 1,338 records. Random Forest won with R² = 0.865 and MAE = $2,674; linear models plateaued at R² ≈ 0.78 due to unmodelled smoker × BMI interaction.
Python · scikit-learn · Pipeline · GridSearchCV · Random Forest · Ridge/Lasso
An AI agent that reads a job description and a folder of résumés, extracts structured candidate data with Pydantic schemas, scores each applicant against JD requirements, and outputs a ranked shortlist with per-candidate reasoning.
LangChain · Pydantic · OpenAI API · PyPDF2 · Streamlit · Pandas
A lightweight benchmark framework comparing LLM outputs on custom prompts through the Groq API and Python-based evaluation.
Groq API · Python · Prompt Evaluation · LLM Benchmarking
Give the agent a research question — it searches ArXiv, fetches top papers, embeds chunks locally with sentence-transformers, synthesizes a structured answer with citations, and flags contradictions between papers using a LangGraph retry loop.
LangGraph · ArXiv API · sentence-transformers · ChromaDB · Groq · Streamlit
About
I’m a final-year BSc Computer Science student building practical AI systems with a focus on LLMs, RAG, NLP, and Python. My work is centered on understanding how models work and turning ideas into useful projects rather than just experiments.
I enjoy solving problems end to end: from data preparation and model design to evaluation and deployment. My strongest interest lies in creating thoughtful LLM applications that are useful, structured, and grounded in real engineering practice.
Capabilities
Contact
I’m interested in internships, collaborations, and conversations around LLM engineering, NLP systems, and practical GenAI applications. Feel free to reach out through any of the links below.