Data Science / ML engineer from Hyderabad, India. B.Tech in Computer Science (Data Science specialization), 2025. I like shipping models end to end: tracked experiments, tested code, containers, CI, and honest evaluation, not just notebooks.
| Project | What it is | Highlights |
|---|---|---|
| Fraud Detection MLOps | Credit-card fraud detection on the real ULB dataset (284,807 transactions, 0.17% fraud) | PR-AUC 0.735 · cost-based threshold tuning · MLflow registry with a promotion gate · PSI drift monitoring · FastAPI + Docker + CI · live demo |
| Telco Churn MLOps | End-to-end churn pipeline | Optuna tuning · MLflow tracking and registry · SHAP explanations plus a local-LLM narrative endpoint · tuned XGBoost test ROC-AUC 0.841 · live demo |
| Second Brain GraphRAG | Local, private knowledge-graph assistant (Ollama, ChromaDB, NetworkX) | Benchmarks GraphRAG against plain RAG, with the null result written up · laptop-safe low-load mode |
| Local RAG Assistant | Chat with any codebase, fully offline | AST-aware chunking · multi-repo support · Ollama + ChromaDB + FastAPI |
| Image Captioning (CNN + LSTM) | InceptionV3 encoder + LSTM decoder on Flickr8k | Published at ICACTEA-2025 (team lead) |
| Fast-AgingGAN | Face-aging GAN in PyTorch Lightning | Trained on CACD and UTKFace |
Languages: Python, SQL · ML: scikit-learn, XGBoost, PyTorch, TensorFlow, SHAP, Optuna · MLOps: MLflow, FastAPI, Docker, GitHub Actions, pytest · GenAI: Ollama, RAG, ChromaDB, GraphRAG
Building out my portfolio, with a focus on credit risk and fairness, and on measuring RAG systems rather than just building them. Open to entry-level Data Science / ML roles.