Muneeb Khan

Key Skills

Software

Data Annotation Tech

Top Subject Matter

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Top Data Types

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Top Label Types

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Evaluation Rating

Freelancer Overview

I am a detail-oriented full-stack developer with hands-on experience in building AI-powered SaaS applications that leverage advanced data processing and content generation features. My work with the Gemini API and projects involving resume analysis, article writing, and image generation has given me a strong foundation in handling, annotating, and preparing diverse data types for AI and machine learning workflows. I am skilled in Python, SQL, and JavaScript, and have used REST APIs and cloud platforms like AWS to manage and deploy scalable solutions. My background in data analytics and generative AI, combined with my experience in optimizing UI/UX and ensuring data integrity, enables me to contribute effectively to data labeling and AI training data initiatives, especially in domains such as NLP, content analysis, and real-time communication platforms.

Labeling Experience

LLM Evaluation and AI Data Annotation Specialist (Freelancer)

Data Annotation TechTextEvaluation Rating

Evaluated and ranked large language model (LLM) responses based on multiple qualitative criteria in a freelance role. Crafted adversarial and multilingual prompts to test and analyze AI robustness across conversation turns. Produced structured analytical rationales and documented observed model weaknesses in English and Hindi. • Assessed LLM responses for relevance, coherence, reasoning, and adherence to instructions • Designed pressure scenarios and simulated user personas for realistic multi-turn dialogues • Generated detailed feedback on logical gaps, hallucinations, and safety issues • Contributed to multilingual AI model evaluation through Hindi prompt engineering

Evaluated and ranked large language model (LLM) responses based on multiple qualitative criteria in a freelance role. Crafted adversarial and multilingual prompts to test and analyze AI robustness across conversation turns. Produced structured analytical rationales and documented observed model weaknesses in English and Hindi. • Assessed LLM responses for relevance, coherence, reasoning, and adherence to instructions • Designed pressure scenarios and simulated user personas for realistic multi-turn dialogues • Generated detailed feedback on logical gaps, hallucinations, and safety issues • Contributed to multilingual AI model evaluation through Hindi prompt engineering

2025

Education

Work History