I'm a Computer Science graduate from Lovely Professional University focused on building practical Machine Learning and Generative AI systems.
My primary interests are Machine Learning, NLP, LLM applications, RAG systems, and AI engineering. I enjoy taking an idea from a model or algorithm and turning it into a usable application through APIs, vector databases, retrieval pipelines, and deployment.
Currently, I'm strengthening my foundations in ML theory, Python, DSA, system design for AI applications, and production-oriented GenAI engineering while building projects that demonstrate those skills.
- 🔭 Building: RAG, Agentic AI, and ML applications
- 🧠 Learning: LLM systems, AI agents, model evaluation, and ML engineering
- 💬 Interested in: Machine Learning · NLP · RAG · LLMs · AI Engineering
- 📍 India
- 🎯 Open to: ML Engineer · AI Engineer · Applied AI · GenAI Engineer · ML Intern roles
Scikit-learn · Regression · Classification · Clustering · Feature Engineering · Model Evaluation · Hyperparameter Tuning · Random Forest · Gradient Boosting
PyTorch · TensorFlow · Keras · Transformers · BERT · T5 · Flan-T5 · BART · Sentence Transformers
LLMs · RAG · Embeddings · Vector Search · Prompt Engineering · LangChain · LlamaIndex · FAISS · ChromaDB · Pinecone
Python · FastAPI · Streamlit · Gradio · REST APIs · Docker · Git · GitHub
NumPy · Pandas · Matplotlib · OpenCV · MediaPipe · SQL
AI-powered research and patent intelligence system
Built a backend-oriented AI system for discovering and analyzing similar research and patent content using semantic embeddings and clustering.
Highlights
- Built a FastAPI-based backend
- Used
multilingual-e5-largefor semantic embeddings - Implemented semantic similarity scoring
- Applied K-Means clustering to organize related content
- Designed the system around retrieval and similarity-based research workflows
Python · FastAPI · SentenceTransformers · K-Means · NLP
RAG-powered AI system for querying company knowledge
Built a document-aware AI application that combines semantic retrieval with LLM-based reasoning to answer questions over company information.
Highlights
- Implemented document ingestion and retrieval
- Generated semantic embeddings using Sentence Transformers
- Used FAISS for vector similarity search
- Connected retrieved context to an LLM
- Built an interactive Streamlit interface
Python · RAG · SentenceTransformers · FAISS · Streamlit
Memory-enabled AI journaling application
Built an AI journaling system that combines conversational generation with retrieval-based memory so previous journal entries can be used as contextual information.
Highlights
- Implemented semantic memory retrieval
- Used FAISS for vector search
- Integrated LangChain-based retrieval workflows
- Generated context-aware responses
- Added emotional trend visualization
Python · LangChain · FAISS · Flan-T5 · BART · Gradio · Plotly
RAG-based PDF question-answering system
Built a document Q&A application that allows users to upload study material and ask questions using retrieval-augmented generation.
Highlights
- PDF ingestion and text processing
- Semantic document retrieval
- Context-aware question answering
- Interactive Streamlit interface
Python · RAG · LLMs · Streamlit
Real-time computer vision system for classroom behavior analysis
Built a webcam-based computer vision pipeline for detecting different classroom engagement behaviors.
Highlights
- Real-time webcam processing
- MediaPipe-based feature extraction
- CNN/ML classification pipeline
- Multiple behavioral classes
- CPU-oriented inference optimization
Python · TensorFlow · OpenCV · MediaPipe · Scikit-learn
Conversational fitness assistant combining NLP intent classification with generative responses.
Python · BERT · GPT-2 · NLP
Mar 2024 – May 2024
Worked on machine learning models for predicting YouTube advertisement views.
- Built regression pipelines using Python and Scikit-learn
- Performed feature engineering and exploratory data analysis
- Compared ensemble learning approaches
- Worked with Random Forest and Gradient Boosting
- Reported an R² score of 0.82 on the project dataset
Python · Scikit-learn · Pandas · NumPy
Lovely Professional University
2022 – 2026
Tirumala Junior College, Rajahmundry
78%
DR K.K.R. Gowtham E.M. School, Rajahmundry
98%
- Exploratory Data Analysis for Machine Learning — IBM
- NLP with Classification and Vector Spaces — DeepLearning.AI
- Algorithms on Strings — University of California San Diego
- Approximation Algorithms & Linear Programming — University of Colorado Boulder
- Introduction to Programming Using Java — LearnQuest
- 🌐 Co-organized Building with AI developer meetup with Google for Developers
- 👥 Helped grow the developer community to 1,500+ members
- 💡 Participated in Code Beyond Limits by TFUG
- ✍️ Write about AI, LLMs, memory architectures, and emerging AI research
Machine Learning
├── Mathematics & Statistics
├── Model Evaluation
├── Feature Engineering
└── ML System Design
Generative AI
├── RAG
├── Embeddings & Vector Search
├── LLM Evaluation
├── Agents
└── Production AI Applications
Software Engineering
├── Python
├── DSA
├── FastAPI
├── Docker
└── APIs & Backend Systems