Data Science, Machine Learning & Generative AI Engineer
I am a data-driven AI engineer building end-to-end solutions that turn raw data into practical, production-ready products. My work spans machine learning, deep learning, computer vision, NLP, retrieval-augmented generation (RAG), and agentic AI — with a focus on shipping models and applications that solve real business and user problems.
I enjoy working across the full AI lifecycle: exploratory analysis, feature engineering, model experimentation, deployment, and user-facing application development. I have hands-on experience building predictive systems, multimodal AI workflows, document intelligence tools, RAG pipelines, and interactive dashboards using Python and modern ML/AI frameworks.
| Project | What I built | Stack |
|---|---|---|
| AniSense AI | AI-powered video intelligence platform for transcription, semantic search, summarization, and timestamped insight retrieval | Python, FastAPI, ChromaDB, BM25, Whisper, Groq, Gemini, spaCy |
| Customer Churn & Revenue Prediction | Customer churn prediction and retention-value analysis for a streaming platform | Python, scikit-learn, XGBoost, FastAPI, Streamlit, Docker |
| FeelSense | Text-emotion classifier with a bidirectional GRU model and interactive browser-based inference | Python, TensorFlow/Keras, FastAPI, NLP, UI/UX |
| RazeChat | Agentic assistant with tool use, PDF Q&A, web search, and conversational memory | LangGraph, FastAPI, Streamlit, Gemini, FAISS, DuckDuckGo |
| Generative AI Course Context Engine | Offline educational RAG system for course-video Q&A and quiz generation | Whisper, Ollama, BGE-M3, FAISS, FastAPI, cosine retrieval |
| Deepfake Face & Voice Detection | Multimodal deepfake detection system using face and audio classification pipelines | TensorFlow/Keras, OpenCV, MFCC, FastAPI, Streamlit, Docker |
| AI-Powered Content Creation Studio | AI-assisted content generation workflow for ideas, outlines, and drafts | React, TypeScript, FastAPI, PostgreSQL, OpenAI/Gemini APIs |
| Time-Series Demand Forecasting | Demand forecasting application with a trained XGBoost model and interactive dashboard | Python, XGBoost, FastAPI, Streamlit |
| CineMatch | Content-based movie recommendation engine with similarity search | Python, spaCy, TF-IDF, FastAPI, Streamlit |
| Bengaluru House Price Prediction | Regression model for property price prediction and API-based inference | scikit-learn, Flask, Python, HTML/CSS/JS |
| Sports Person Image Classification | Classical computer vision classification for sports personalities | OpenCV, scikit-learn, PyWavelets, Python |
| Student Mental Health Score Prediction | Predictive analytics model for mental-health risk indicators based on behavioral data | Random Forest, FastAPI, HTML/CSS/JS |
- Machine Learning: scikit-learn, XGBoost, Random Forest, SVM, logistic regression, classification, regression, feature engineering, model evaluation, hyperparameter tuning
- Deep Learning: TensorFlow, Keras, CNNs, RNNs, LSTM, GRU, BiGRU, computer vision, multimodal AI
- NLP & Generative AI: Whisper, spaCy, Hugging Face, prompt engineering, LLM integrations, text generation, multimodal workflows
- RAG & AI Agents: embeddings, vector search, FAISS, ChromaDB, BM25, semantic retrieval, LangGraph, tool-using agents, document Q&A
- Data & Analytics: Python, pandas, NumPy, SQL, exploratory data analysis, visualization, dashboarding
- Software & Deployment: FastAPI, Flask, Streamlit, React, TypeScript, PostgreSQL, Docker, GitHub Actions, Jupyter
| python | scikit-learn | tensorflow | spacy | langchain | langgraph | docker | GCP | git | Github | SQL | VectorDB |
I am passionate about building intelligent systems that combine analytical rigor with practical product thinking. I enjoy turning complex data and AI ideas into intuitive tools that people can actually use.