I am a data science enthusiast with hands-on experience as a Data Science Intern at AI Variant, Bangalore, and a background as a Biomedical Engineer at Indraprastha Apollo Hospitals, New Delhi, and Service Engineer at Kirloskar Technologies, Jaipur. Holding a Bachelor's degree in Electrical Engineering, I specialize in data analysis, cleaning, visualization, statistical modeling, SQL, NLP, and machine learning. I am actively exploring modern AI technologies including Generative AI, Retrieval-Augmented Generation (RAG), and LLM-based solutions. Passionate about extracting insights from complex data to drive innovation and impactful business decisions. 

• Developed a multipurpose AI chatbot using Flask and Chainlit with a popup chat UI for seamless interaction.

• Integrated LangChain-based RAG pipeline with FAISS vector store, Groq LLMs, and PDF document ingestion via HuggingFace embeddings.

• Built for domain-specific Q&A, supporting healthcare and technical documents with fast semantic search and real-time responses. 

• Developed a real-time Facial Emotion Detector using FastAPI and MediaPipe for accurate face tracking.

• Integrated multi-provider AI inference with Hugging Face models, Groq LLMs, and OpenRouter APIs to support 7-class emotion recognition.

• Deployed on Render with Docker for production-ready scalability, featuring live webcam analysis, image uploads, and interactive UI with Tailwind CSS. 

• It's an AI-powered assistant built using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).

• It analyzes PDF service manuals of biomedical machines to provide precise repair and troubleshooting support.

• Designed to help clinical engineers quickly resolve device issues with step-by-step, context-based guidance.

• Engineered personalized book recommendations using collaborative and content-based filtering. Using NLP techniques and tools such as Python, NLTK, and SpaCy to extract insights and similarities between the books.

• The analysis was conducted efficiently, saving time and eliminating the need for manual data processing. The outcome was the ability to recommend similar books based on similar books and genres.

• Extend the parser to handle various resume formats (e.g., PDF, DOCX, TXT) to accommodate a wider range of submissions. 

• Implemented an NLP model to extract and structure resume content, improving HR efficiency. Customized entity recognition and skill matching for precise candidate selection. 

• Tailored entity recognition and skill matching, ensuring the parser selects candidates with utmost accuracy for a refined recruitment process. 

• Applied advanced algorithms to identify global socio-economic patterns.

• Uncovered actionable insights for targeted policy interventions by determining distinct clusters within world development data.

• Applied advanced feature selection techniques to enhance cluster accuracy and interpretability in identifying global socio-economic patterns. 

• Embark on a data-driven journey with this Nifty50 web scraping repository, where Python's Requests and BeautifulSoup libraries join forces. 

• Streamlining HTTP requests and HTML parsing, this dynamic duo enables developers to effortlessly retrieve and extract precise data from Nifty50 websites, empowering efficient and effective web scraping endeavors.