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Laiba Mughal

Laiba Mughal

Data/Business Analyst | Machine Learning & Deep Learning Engineer | RAG/LLM Developer

Pakistan flagRahim Yar Khan, Pakistan
$10.00/hrIntermediateLabelboxScale AI

Key Skills

Software

LabelboxLabelbox
Scale AIScale AI

Top Subject Matter

Large Language Model (LLM) Training and Adaptation
Medical Question Answering – AI Diagnostic Chatbot
Information Retrieval and Document Question Answering (QA)

Top Data Types

TextText
DocumentDocument
ImageImage

Top Task Types

Fine-tuningFine-tuning
Question AnsweringQuestion Answering
ClassificationClassification
Computer Programming/CodingComputer Programming/Coding
Text GenerationText Generation

Freelancer Overview

Freelance Data Analyst & Machine Learning Developer – Skilled in Data Analysis, Machine Learning, and AI solution development with experience building predictive models, data-driven applications, and intelligent workflows. Strong background in data preprocessing, model training, feature engineering, and analytics-driven problem solving. Familiar with ML frameworks and AI tools including LangChain and LLM-based systems. Education includes Bachelor of Science, Khwaja Fareed University of Engineering and Information Technology (2023). AI training experience includes working with text/document datasets, fine-tuning workflows, question answering systems, and data labeling.

IntermediateEnglishUrdu

Labeling Experience

Freelance AI/ML Developer – LLM Fine-tuning and RAG System Deployment

TextFine Tuning
Executed LLM fine-tuning projects, adapting foundation models for domain-specific tasks. Built and deployed multiple LLM-powered chatbots and AI assistants, focusing on supervised model training from raw data to production. Engineered and deployed RAG systems for intelligent document querying using vector embeddings. • Led the implementation of prompt engineering and API integration for LLM applications. • Developed and executed complete project pipelines including data preprocessing and evaluation. • Adapted foundation models to enhance accuracy for domain-specific client needs. • Handled architecture design, model optimization, and deployment for data-driven solutions.

Executed LLM fine-tuning projects, adapting foundation models for domain-specific tasks. Built and deployed multiple LLM-powered chatbots and AI assistants, focusing on supervised model training from raw data to production. Engineered and deployed RAG systems for intelligent document querying using vector embeddings. • Led the implementation of prompt engineering and API integration for LLM applications. • Developed and executed complete project pipelines including data preprocessing and evaluation. • Adapted foundation models to enhance accuracy for domain-specific client needs. • Handled architecture design, model optimization, and deployment for data-driven solutions.

2024 - Present

Academic Project: MedBot—AI Medical Chatbot (RAG-based)

DocumentQuestion Answering
Developed a retrieval-augmented generation (RAG) based AI chatbot MedBot for disease prediction and symptom analysis from medical textbooks. System enabled document ingestion and intelligent querying for medical diagnostic support. Achieved high diagnostic accuracy through iterative tuning and data selection. • Collected and curated medical text datasets for ingestion. • Utilized vector embeddings for precise document retrieval. • Labeled and validated diagnosis outputs based on clinical benchmarks. • Integrated the solution using Python, LangChain, Gemini API, and FAISS.

Developed a retrieval-augmented generation (RAG) based AI chatbot MedBot for disease prediction and symptom analysis from medical textbooks. System enabled document ingestion and intelligent querying for medical diagnostic support. Achieved high diagnostic accuracy through iterative tuning and data selection. • Collected and curated medical text datasets for ingestion. • Utilized vector embeddings for precise document retrieval. • Labeled and validated diagnosis outputs based on clinical benchmarks. • Integrated the solution using Python, LangChain, Gemini API, and FAISS.

2024 - 2025

Academic Project: RAG System – Document QA and Retrieval

DocumentQuestion Answering
Implemented an RAG system for Document QA over large PDF datasets using LangChain and FAISS. Enabled intelligent document querying and retrieval for unstructured information extraction. Achieved over 90% retrieval accuracy with labeled training sets. • Selected and annotated key query-document pairs for training. • Tuned the retrieval process using vector embedding techniques. • Evaluated system accuracy using ground truth answers. • Developed and maintained internal labeling workflow and data integrity.

Implemented an RAG system for Document QA over large PDF datasets using LangChain and FAISS. Enabled intelligent document querying and retrieval for unstructured information extraction. Achieved over 90% retrieval accuracy with labeled training sets. • Selected and annotated key query-document pairs for training. • Tuned the retrieval process using vector embedding techniques. • Evaluated system accuracy using ground truth answers. • Developed and maintained internal labeling workflow and data integrity.

2023 - 2024

Education

K

Khwaja Fareed University of Engineering and Information Technology

Bachelor of Science, Information Technology

Bachelor of Science
2023

Work History

I

Independent

Freelance AI/ML Developer

Rahim Yar Khan
2024 - Present
1

10Pearls

Machine Learning Engineer Intern

Rahim Yar Khan
2024 - 2024