📧 sk003cs@gmail.com
🔗 LinkedIn

1. Salesforce Co-Pilot
   Demonstrates the capabilities of Salesforce Co-Pilot in automating customer service tasks using advanced AI technologies.
   

2. Salesforce Co-Pilot: Adding Interactive Charts
   Showcases how to enhance Salesforce applications with interactive charts to visualize data and provide insightful analytics.
   

3. Chatting with Complex PDFs (GST) - GPT-4o vs RAG-based AI
   A comparison of how GPT-4o and RAG-based AI handle complex documents, such as GST PDFs, showcasing the capabilities of RAG-based approaches.
   

4. GPT-4o vs RAG-based AI
   An in-depth analysis of GPT-4o versus RAG-based AI, highlighting their strengths and use cases.
   

5. Adaptive Learning - Adjust Difficulty of Question Based on Student Previous Answer
   Demonstrates an adaptive learning system that dynamically adjusts the difficulty of questions based on a student's previous responses, providing a personalized learning experience.
   

6. Crawler Bot: Developed a parallel crawler bot capable of extracting data from static and dynamic webpages. It performs multi-threaded crawling, allowing for rapid data extraction and processing. This bot is designed to enhance the knowledge base by gathering extensive data efficiently.

7. Knowledge Base Bot: Built a sophisticated knowledge base bot that uses AI to deliver accurate information from extensive databases and documents. This bot combines the power of large language models with a rich repository of data to provide contextually relevant answers to user queries.

8. Real-Time Bot: Created a real-time bot for instant user interactions, offering immediate responses based on current data. This bot is integrated with RAG-based systems, ensuring it provides up-to-date and contextually accurate information, enhancing user engagement and satisfaction.

I recently developed an AI-Driven Book Management System using Python, FastAPI, LangChain, and AWS infrastructure. This project showcases my expertise in integrating AI models, cloud deployment, and asynchronous programming. Key features include:

• Database Management: Utilized AWS RDS PostgreSQL for efficient storage and retrieval of books and reviews.
• AI-Generated Summaries: Integrated the Llama3 model, deployed on AWS SageMaker, for generating summaries of books based on their content.
• Personalized Recommendations: Implemented two key scenarios:
  • Scenario 1: Recommending books based on user preferences and written reviews using pgvector embeddings.
  • Scenario 2: Providing tailored book suggestions based solely on user preferences for those without reviews.
• Asynchronous Programming: Enhanced performance by implementing asynchronous operations for database interactions and AI model predictions using sqlalchemy[asyncio] and asyncpg.
• Embedding Generation and Similarity Search: Utilized the HuggingFace model distilbert-base-nli-mean-tokens via a serverless API for generating embeddings, and performed similarity searches against book content.
• Content Extraction: Employed LangChain loaders for extracting book content from files.
• API Development and Security: Developed a secure RESTful API using FastAPI with JWT token protection, providing comprehensive documentation via Swagger.
• Dockerization: Containerized the application using Docker for easy deployment and scalability

This project demonstrates my ability to build and deploy advanced AI systems, ensuring secure and efficient operations.

I specialize in building advanced RAG-based AI systems that combine the power of large language models with real-time data retrieval to provide accurate and contextually relevant information. My work includes:

• Designing and implementing RAG pipelines that leverage vector databases (Milvus, Pinecone, Faiss) for efficient information retrieval.
• Developing AI solutions for various use cases, including knowledge-based question answering, e-commerce, and personalized customer support.
• Integrating AI capabilities into existing systems to enhance functionality and user engagement.
• Utilizing LangChain to streamline the creation and deployment of complex AI workflows.

These solutions are designed to improve efficiency, accuracy, and user satisfaction by dynamically generating responses based on both stored and real-time data.

With a passion for creating intuitive and responsive user interfaces, I have extensive experience using ReactJS to develop scalable and maintainable web applications. My contributions include:

• Building complex UIs that are both visually appealing and highly functional.
• Implementing state management solutions to handle dynamic data and user interactions efficiently.
• Collaborating with backend teams to ensure seamless integration of APIs and other services.
• Ensuring cross-browser compatibility and optimizing application performance.

My goal is to deliver a user experience that is both engaging and efficient, contributing to the overall success of the application and business objectives.

• Frontend Development: ReactJS, AngularJS, HTML5, Bootstrap
• Backend Development: Python, FastAPI, Java Spring Boot
• AI & Machine Learning: LangChain, PyTorch, TensorFlow, Gradio
• Databases: Milvus, Pinecone, Faiss, MySQL, DynamoDB, MongoDB
• DevOps & Tools: Docker, Nginx, Git, AWS Services, WebSocket, Postman
• Other Technologies: Chrome Extensions, Salesforce LWC, SOQL, APEX