Afifa Qadri

Key Skills

Software

Labelbox

Lionbridge

Telus

Internal/Proprietary Tooling

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Video

Top Label Types

Action Recognition

Fine Tuning

Prompt Response Writing SFT

RLHF

Translation Localization

Freelancer Overview

I have hands-on experience in AI training and data labeling across text, search, audio, and video modalities, with a strong focus on quality and accuracy. My work includes LLM response evaluation, text annotation, search relevance rating, transcription review, and video annotation for real-world AI systems. I’ve contributed to projects where attention to linguistic nuance, intent matching, and consistency was critical to improving model performance. I bring a quality-first mindset, supported by multilingual proficiency in English, Hindi, and Urdu, and experience working with proprietary annotation tools on large-scale platforms. I’m particularly strong in QA, guideline adherence, and edge-case identification, and I’m comfortable reviewing, correcting, and validating training data to meet strict client standards.

Labeling Experience

Chatbot Response Comparison & Evaluation

Data Annotation TechTextQuestion AnsweringText Summarization

Compared outputs from two AI chatbots for the same user queries, evaluating responses for accuracy, relevance, safety, coherence, and guideline compliance. Annotated and rated responses to identify inconsistencies, strengths, and areas for improvement, supporting fine-tuning and quality enhancement of the models. Ensured consistent and reliable evaluation across multiple rounds of assessment.

Compared outputs from two AI chatbots for the same user queries, evaluating responses for accuracy, relevance, safety, coherence, and guideline compliance. Annotated and rated responses to identify inconsistencies, strengths, and areas for improvement, supporting fine-tuning and quality enhancement of the models. Ensured consistent and reliable evaluation across multiple rounds of assessment.

2025 - 2025

Video Action Annotation for Nvidia AI

Internal Proprietary ToolingVideoClassificationEmotion Recognition

Annotated video data at a granular, frame-by-frame level by identifying and labeling each individual human action and activity. Followed detailed project guidelines to ensure temporal accuracy, consistency across frames, and correct action boundaries. Performed quality checks to meet strict annotation standards and reduce ambiguity in training data used for model learning and evaluation.

Annotated video data at a granular, frame-by-frame level by identifying and labeling each individual human action and activity. Followed detailed project guidelines to ensure temporal accuracy, consistency across frames, and correct action boundaries. Performed quality checks to meet strict annotation standards and reduce ambiguity in training data used for model learning and evaluation.

2024 - 2025

LLM Response Evaluation & Text Annotation

Internal Proprietary ToolingTextEntity Ner ClassificationClassification

Reviewed and annotated AI-generated text responses for accuracy, relevance, safety, and guideline compliance. Performed entity recognition, text classification, and summarization tasks to improve large language model performance. Conducted quality checks to ensure consistency and reduce errors in training data.

Reviewed and annotated AI-generated text responses for accuracy, relevance, safety, and guideline compliance. Performed entity recognition, text classification, and summarization tasks to improve large language model performance. Conducted quality checks to ensure consistency and reduce errors in training data.

2022 - 2024

Personalized Ads Assessor

TelusTextClassificationEvaluation Rating

Reviewed and evaluated online advertisements and search results for relevance, accuracy, and compliance with guidelines. Classified ads based on content, intent, and quality to support AI systems in improving ad targeting and user experience. Conducted quality checks to ensure consistent labeling across multiple campaigns.

Reviewed and evaluated online advertisements and search results for relevance, accuracy, and compliance with guidelines. Classified ads based on content, intent, and quality to support AI systems in improving ad targeting and user experience. Conducted quality checks to ensure consistent labeling across multiple campaigns.

2021 - 2023

Education

Work History