Hamna Zahid

Key Skills

Software

Label Studio

Roboflow

Labelbox

Scale AI

Top Subject Matter

AI/ML Model Training (Domain-Specific LLMs)

Business Analytics / Machine Learning

Fashion Recommendation AI

Top Data Types

Video

Text

Computer Code Programming

Image

Top Task Types

RLHF

Fine Tuning

Red Teaming

Evaluation Rating

Computer Programming Coding

Text Summarization

Text Generation

Object Detection

Question Answering

Classification

Segmentation

Entity Ner Classification

Freelancer Overview

Data / AI Engineer (Training Data Annotation Lead). Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Label Studio, Internal, and Proprietary Tooling. Education includes Bachelor of Science, Islamia University. AI-training focus includes data types such as Text and labeling workflows including Fine-tuning and Entity (NER) Classification.

Labeling Experience

Data / AI Engineer (Training Data Annotation Lead)

Label StudioTextFine Tuning

Managed annotation pipelines with Label Studio for training data curation and quality control. Oversaw the design and fine-tuning of domain-specific LLMs using LoRA/QLoRA adapters. Ensured high data quality standards in end-to-end AI model training workflows. • Developed guidelines for annotators to maintain consistent labeling • Conducted QA and validation cycles to review labeled datasets • Integrated labeled data into model training pipelines • Collaborated with engineering teams on labeling workflow optimization

Managed annotation pipelines with Label Studio for training data curation and quality control. Oversaw the design and fine-tuning of domain-specific LLMs using LoRA/QLoRA adapters. Ensured high data quality standards in end-to-end AI model training workflows. • Developed guidelines for annotators to maintain consistent labeling • Conducted QA and validation cycles to review labeled datasets • Integrated labeled data into model training pipelines • Collaborated with engineering teams on labeling workflow optimization

2025 - Present

Data annotator

VideoComputer Programming Coding

Developed and managed training datasets for physics-informed neural networks (PINNs) applied to transient heat conduction in composite materials. Tasks included structuring PDE collocation point data, labeling boundary condition inputs, and evaluating model output quality across training runs — directly analogous to RLHF preference ranking and evaluation rating tasks. Additionally worked on video action recognition using VideoMamba on the UCF-101 dataset, involving frame-level data preprocessing, motion map annotation, and model output evaluation. All work conducted in Python and PyTorch with consistent attention to data quality, labeling consistency, and structured guideline adherence.

Developed and managed training datasets for physics-informed neural networks (PINNs) applied to transient heat conduction in composite materials. Tasks included structuring PDE collocation point data, labeling boundary condition inputs, and evaluating model output quality across training runs — directly analogous to RLHF preference ranking and evaluation rating tasks. Additionally worked on video action recognition using VideoMamba on the UCF-101 dataset, involving frame-level data preprocessing, motion map annotation, and model output evaluation. All work conducted in Python and PyTorch with consistent attention to data quality, labeling consistency, and structured guideline adherence.

2025 - Present

Project Lead (LLM Annotation Pipeline)

Label StudioTextFine Tuning

Designed and maintained an end-to-end data labeling and versioning pipeline for NLP training data. Automated quality assurance checks to enhance dataset integrity before model training. Exported finalized datasets to Hugging Face Hub for open benchmarking and collaborations. • Set up multi-stage labeling workflows in Label Studio and CVAT • Automated pipeline syncing using Airflow and DVC • Enforced labeling best practices in annotation guidelines • Ensured review process caught annotation inconsistencies

Designed and maintained an end-to-end data labeling and versioning pipeline for NLP training data. Automated quality assurance checks to enhance dataset integrity before model training. Exported finalized datasets to Hugging Face Hub for open benchmarking and collaborations. • Set up multi-stage labeling workflows in Label Studio and CVAT • Automated pipeline syncing using Airflow and DVC • Enforced labeling best practices in annotation guidelines • Ensured review process caught annotation inconsistencies

2024 - 2024

Project Contributor (Wardo – AI Stylist & Marketplace)

Label StudioTextFine Tuning

Fine-tuned a fashion recommendation LLM using a custom-annotated style dataset for AI-powered recommendations. Managed the end-to-end annotation process to ensure high model accuracy. Oversaw dataset versioning and export for reproducible benchmarking. • Annotated style-related text samples with fashion-specific tags • Maintained clean labeling guidelines for domain-specific data • Imported/exported annotations using Label Studio pipelines • QA validation performed on annotated data pre-training

Fine-tuned a fashion recommendation LLM using a custom-annotated style dataset for AI-powered recommendations. Managed the end-to-end annotation process to ensure high model accuracy. Oversaw dataset versioning and export for reproducible benchmarking. • Annotated style-related text samples with fashion-specific tags • Maintained clean labeling guidelines for domain-specific data • Imported/exported annotations using Label Studio pipelines • QA validation performed on annotated data pre-training

2023 - 2023

Business Analyst (Data Annotation Workflows)

TextEntity Ner Classification

Defined data annotation requirements and created structured labeling workflows for ML training datasets. Worked closely with engineering teams to ensure annotated data met modeling needs. Established and maintained documentation for consistent annotation practices. • Designed custom labeling instructions for dataset annotators • Set up QA review process for annotation quality • Coordinated between analysts and annotation contractors • Reported on annotation progress to project stakeholders

Defined data annotation requirements and created structured labeling workflows for ML training datasets. Worked closely with engineering teams to ensure annotated data met modeling needs. Established and maintained documentation for consistent annotation practices. • Designed custom labeling instructions for dataset annotators • Set up QA review process for annotation quality • Coordinated between analysts and annotation contractors • Reported on annotation progress to project stakeholders

2023 - 2023

Education

Work History