Fatema Tuj Johora Faria

Research Experience

Remote Research Assistant
Supervisor: Dr. Laith H. Baniata
Research Professor, Gachon University, South Korea
Email: laith@gachon.ac.kr
June 2024 - Present

■ Currently working on the research titled "Towards Robust Chain-of-Thought Prompting with Self-Consistency for Remote Sensing VQA: An Empirical Study Across Large Multimodal Models."
■ Investigated research on "Analyzing Diagnostic Reasoning of Vision-Language Models via Zero-Shot Chain-of-Thought Prompting in Medical Visual Question Answering." This research was supported by the National Institute of Health (NIH) research project in South Korea (Project No. 2024ER080300), and also by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT under Grant NRF-2022R1A2C1005316.
■ Conducted research on "Investigating the Predominance of Large Language Models in Low-Resource Bangla Language Over Transformer Models for Hate Speech Detection: A Comparative Analysis." This work was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT under the grant NRF-2022R1A2C1005316.
■ Carried out research on "SentimentFormer: A Transformer-Based Multi-Modal Fusion Framework for Enhanced Sentiment Analysis of Memes in the Under-Resourced Bangla Language." This work was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT under the grant NRF-2022R1A2C1005316.

Professional Experience

Senior Application Developer
Dexian (Bangladesh) Limited.
July 2025 - Present

■ Conversational Agent Platform: ShareFlow Agent (Ongoing)
- ○ Developed a custom SharePoint-integrated ReAct Agentic RAG system that enables users to create their own tools by providing the agent name, instructions, description, and uploaded files.
- ○ Implemented session-based chat functionality ensuring each user's conversations with individual agents are kept separate, with full history retention for context-aware interactions.
- ○ Generated leading questions based on the agent's instructions and description to guide user interactions.
- ○ Integrated a user interface to display the list of agents created by the user or shared with them, along with an update feature that allows users to modify existing agents.
- ○ Designed a sharing functionality that allows users to share their agents with others for collaborative use.
- ○ Guided and mentored junior application developers to support their technical development, promote best practices, and ensure the delivery of high-quality, maintainable solutions.
- ○ Tech Stack Used: Microsoft SharePoint, Python, LlamaIndex, Azure OpenAI, Azure SQL, AlloyDB, React JS, FastAPI

Application Developer
Dexian (Bangladesh) Limited.
May 2024 - July 2025

■ Organizational Intelligence Role Placement System: Org Info
- ○ Implemented a multimodal agent for extracting organizational hierarchical information from organograms, utilizing in-context learning with tree-of-thought prompting to preserve the correct parent-child structure.
- ○ Applied multipath reasoning to resolve conflicts and ambiguities in relationships for accurate role placement and stored the extracted hierarchical information in a relational database.
- ○ Designed an LLM-based agent that converted natural language queries into SQL using few-shot learning and self-consistency with chain-of-thought prompting, which enabled contextual reasoning to accurately retrieve relevant organizational data and integrated the results into the OrgChart framework for hierarchical visualization.
- ○ Developed a dynamic Agentic RAG-guided chat interface that enabled users to interact with a specific organizational hierarchy by utilizing predefined query types, roles, and goals, and delivered context-aware, natural language responses.
- ○ Set up scheduled jobs to fetch organizational data from Bullhorn every 30 days and visualized organizational information in OrgChart.
- ○ Reduced search time for organizational hierarchies by 92% through eliminating full Bullhorn database queries.
- ○ Tech Stack Used: Python, LangChain, LangGraph, Azure OpenAI, OpenCV, Azure SQL, React JS, FastAPI
■ Next-Gen Proposal Automation Engine: RFPMatcher
- ○ Developed a Retrieval-Augmented Generation (RAG) solution using Chain of Thought prompting to extract key information from Request for Proposal (RFP) documents.
- ○ Built a Past Experience Matcher system that uses Automatic Chain-of-Thought prompting alongside in-context learning and preset questions to extract requirements from RFPs, then matches them against a master database of prior proposal responses.
- ○ Enabled the system to generate Yes/No decisions with detailed explanations of how similar requirements were addressed in the past, aiding in the prediction of potential win/loss outcomes for new proposals.
- ○ Generated dynamic Tables of Contents (TOC) based on extracted key information and historical experience to streamline and structure the proposal writing process for new bids.
- ○ Reduced manual review time by 75% via automated extraction and predictive insights for Proposal Managers.
- ○ Tech Stack Used: Python, LlamaIndex, Azure OpenAI, AlloyDB, CouchDB, React JS, FastAPI
■ Automated Presentation Insights Generator: CaseAligner
- ○ Built an LLM-based application using zero-shot prompting to generate PowerPoint presentations for case studies based on selected practice areas and industries.
- ○ Implemented an interactive chat interface allowing users to query specific slide content and receive instant contextual responses.
- ○ Developed comprehensive search functionality to locate information across all generated case studies.
- ○ Created export capabilities for downloading slides in company's official template.
- ○ Designed an admin panel for authorized users to download and edit the knowledge base of case studies.
- ○ Tech Stack Used: Python, LlamaIndex, Azure OpenAI, React JS, FastAPI
■ Legal Document Information Retrieval System: KnowledgeEngine
- ○ Developed an LLM-based, multi-document RAG Q&A system for internal document information retrieval.
- ○ Implemented a chat conversation interface with document page references for information sources.
- ○ Maintained session-based dedicated knowledge bases to ensure data isolation and user-specific context management.
- ○ Created an admin panel with document upload functionality and comprehensive document management capabilities.
- ○ Tech Stack Used: Python, LlamaIndex, Azure OpenAI, AlloyDB, React JS, FastAPI
■ Smart Recruitment Analytics Tool: AgentDexi
- ○ Designed an LLM-based multi-agent system that scrapes job postings and analyzes demand trends to generate customer intelligence reports with graphical charts.
- ○ Developed an RAG solution to identify technological trends in job descriptions across external companies.
- ○ Minimized analysis time by 80% by helping technical recruiters gain insights and optimized hiring strategies.
- ○ Tech Stack Used: Python, LangChain, CrewAI, Azure OpenAI, React JS, FastAPI