Mohammad Afaque

Key Skills

Software

Appen

Data Annotation Tech

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Video

Top Label Types

Audio Recording

Classification

Data Collection

Evaluation Rating

Object Detection

Prompt Response Writing SFT

Question Answering

Text Generation

Transcription

Translation Localization

Freelancer Overview

I am an experienced AI Trainer and Data Annotation Specialist with over 6 years in training, evaluating, and improving AI systems, particularly in Natural Language Processing and conversational AI. My expertise spans prompt engineering, dataset labeling, Side-by-Side (SxS) response evaluation, and reinforcement learning from human feedback (RLHF). I have a strong track record of producing high-quality annotated datasets and providing analytical feedback that enhances model accuracy, instruction-following, and safety. Skilled in multilingual evaluation (English, Hindi, Urdu), I have collaborated with global teams on diverse projects, including chatbot training, search engine evaluation, and content moderation. My work is characterized by attention to detail, guideline compliance, and a commitment to quality control using leading annotation and evaluation platforms.

Labeling Experience

AI Data Annotator / Data Labeler (Freelance)

Data Annotation TechAudioClassificationText Generation

As a freelance AI Data Annotator and Data Labeler, I performed comprehensive text annotation, classification, and dataset labeling for various AI and NLP projects. My work involved preparing and validating training datasets, following strict annotation guidelines, and ensuring data quality for downstream machine learning applications. I specialized in conversational data labeling, multilingual annotation, and search result evaluation to improve AI performance. • Labeled and classified text samples for intent, sentiment, relevance, and content category. • Annotated conversational datasets for chatbot and conversational AI training. • Conducted search engine evaluation to refine relevance and ranking of results. • Reviewed datasets in English, Hindi, and Urdu for accuracy and guideline compliance.

As a freelance AI Data Annotator and Data Labeler, I performed comprehensive text annotation, classification, and dataset labeling for various AI and NLP projects. My work involved preparing and validating training datasets, following strict annotation guidelines, and ensuring data quality for downstream machine learning applications. I specialized in conversational data labeling, multilingual annotation, and search result evaluation to improve AI performance. • Labeled and classified text samples for intent, sentiment, relevance, and content category. • Annotated conversational datasets for chatbot and conversational AI training. • Conducted search engine evaluation to refine relevance and ranking of results. • Reviewed datasets in English, Hindi, and Urdu for accuracy and guideline compliance.

2020

AI Quality Analyst (QA) (Deepgram)

AppenTextEvaluation RatingAudio Recording

As an AI Quality Analyst at Deepgram, I was responsible for reviewing and ensuring the quality of annotation work submitted by contributors. My duties included verifying guideline compliance, identifying inconsistencies, and supporting the generation of high-quality datasets for AI model training. I provided detailed feedback to contributors to help improve overall dataset quality. • Reviewed annotation work to ensure rigorous application of guidelines. • Identified discrepancies and proposed corrections for quality improvement. • Supported dataset creation used in AI model training and refinement. • Promoted among 100 contributors for exemplary quality control.

As an AI Quality Analyst at Deepgram, I was responsible for reviewing and ensuring the quality of annotation work submitted by contributors. My duties included verifying guideline compliance, identifying inconsistencies, and supporting the generation of high-quality datasets for AI model training. I provided detailed feedback to contributors to help improve overall dataset quality. • Reviewed annotation work to ensure rigorous application of guidelines. • Identified discrepancies and proposed corrections for quality improvement. • Supported dataset creation used in AI model training and refinement. • Promoted among 100 contributors for exemplary quality control.

2019 - 2020

Education

Work History