Computer Engineering student building intelligent systems — from LLM-powered retrieval pipelines to production-grade NLP classifiers. I care about models that ship, not just models that score well in a notebook.
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🧠 Generative AI & LLMs Building RAG pipelines and context-aware applications |
🔎 NLP & Text Intelligence Classification, embeddings, and explainability (SHAP) |
📊 Applied Data Science Turning raw data into decision-ready insights |
Languages
AI / ML
Generative AI
Data
Backend / DevOps
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Retrieval-Augmented Generation platform answering healthcare questions with context-aware, source-grounded responses.
Stack: LangChain · FAISS · ChromaDB · Sentence Transformers · Gemini · FastAPI · Docker Highlights: |
Text classification system detecting depression, anxiety, suicidal ideation, and emotional state from text, with explainable predictions. Stack: Hugging Face Transformers · DistilBERT · SHAP · Scikit-learn · Streamlit · Supabase Highlights: |
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AI-based digital forensics system detecting manipulated images, video, audio, and documents. Stack: Computer Vision · Deep Learning · Image Processing Highlights: |
More projects on my GitHub profile → |
| Domain | |
|---|---|
| Natural Language Processing | 🟢🟢🟢🟢🟢 |
| Generative AI / RAG | 🟢🟢🟢🟢🟢 |
| Computer Vision | 🟢🟢🟢⚪⚪ |
| Data Science & Analytics | 🟢🟢🟢🟢⚪ |
Reflects current focus and depth of hands-on project work — not a measured or certified skill score.
- Advanced Data Structures & Algorithms
- Advanced Generative AI techniques
- Production-grade RAG system design
- MLOps & model deployment
- Scalable AI system architecture
- Cloud deployment (AWS / GCP)
"I build systems that don't just predict — they explain, adapt, and hold up in production."