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MCP-Build-Rich-Context-AI-Apps-with-Anthropic
May 17, 2025 – May 17, 2025
Model Context Protocol (MCP), an open standard developed by Anthropic to simplify the integration of AI applications with external tools and data sources
View ProjectBook-Writing-AI-Agent
April 30, 2025 – November 4, 2025
An intelligent, multi-agent system to autonomously plan, write, edit, fact-check, and publish a complete book using the CrewAI framework and Groq's LLaMA-3-70B model. This AI pipeline mimics the collaborative process of human authors, editors, and publishers to streamline creative book development.
View ProjectDocling__LLamaIndex_RAG
November 23, 2024 – November 26, 2024
Docling parses documents and exports them to the desired format with ease and speed.Reads popular document formats (PDF, DOCX, PPTX, XLSX, Images, HTML, AsciiDoc & Markdown) and exports to Markdown and JSON
View ProjectMultimodel_Search_RAG
November 2, 2024 – November 13, 2024
multimodal search and Retrieval-Augmented Generation (RAG) have transformed the way we search and retrieve information by enabling the combination of multiple data types, such as text, images, and audio. Multimodal search leverages this diversity of data to provide more accurate and contextually rich search results
View ProjectBuild-and-Evaluate-Advanced-RAG
August 24, 2024 – Present
This project focuses on building and evaluating advanced Retrieval-Augmented Generation (RAG) techniques. RAG is a powerful approach that combines the strengths of information retrieval and generative models to produce more accurate and contextually relevant responses.
View ProjectLLamaIndex-Web-Page-Reader
August 21, 2024 – August 21, 2024
Utilizing LlamaIndex and Google Gemini's Multiple Models , We create a Web Page Reader ,where we provide a URL of a Web Page and the models will be able to understand and fetch the required data.
View ProjectML_Model_Web_Application
July 7, 2024 – January 19, 2025
This project, developed by Invergence Analytics, aims to predict managers who are likely to switch to other funds. The dataset, created by our data experts and SMEs, contains 120 features and 460,000 records. Due to the real-world nature of the data, it is highly imbalanced, with fewer instances of managers switching funds
View ProjectUnlocking-the-Power-of-NLP-Automatic-Question-Generation
September 2, 2023 – September 2, 2023
Question generation is a challenging natural language processing task that involves creating meaningful questions from provided context and answers. This project showcases how to perform this task effectively using transformer-based models, Flash Text for keyword extraction, and the T5 model for question generation.
View ProjectFake-News-Detection-Using-NLP-and-BERT
July 4, 2023 – Present
This project focuses on the detection of fake news using Natural Language Processing (NLP) techniques and BERT (Bidirectional Encoder Representations from Transformers) model. The goal is to build a machine learning model that can accurately classify news articles as either fake or true based on their textual content.
View ProjectEnd-to-End-Time-Series-ForeCasting-Project
June 20, 2023 – June 20, 2023
This project focuses on forecasting the volume of Apple stock traded using Time Series Forecasting techniques. The dataset used contains the stock price data of Apple from the NASDAQ stock exchange for selected time periods in 2018 and 2019.
View ProjectCultural Fit Analysis
The candidate's project portfolio demonstrates a strong interest and practical application in the Data Scientist domain, particularly in NLP and LLMs. The diversity of projects, from fake news detection to AI agents and RAG systems, suggests a proactive and curious individual. However, the projects are all personal and lack information on team collaboration or real-world business impact, which are crucial for cultural fit in many organizations. The absence of professional experience or education makes it difficult to fully assess alignment with a collaborative work culture.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate a strong technical focus, but there is no information regarding teamwork, problem-solving approaches, or communication style in a collaborative environment.