Naseem Akhtar

Key Skills

Software

Data Annotation Tech

Top Subject Matter

Large Language Models (LLMs)

Bilingual AI

Natural Language Processing

Top Data Types

Text

Document

Top Task Types

Fine Tuning

Evaluation Rating

Classification

Freelancer Overview

Freelance AI Data Specialist. Brings professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Data Annotation Tech. Education includes Master of Computer Application, Lovely Professional University (2024) and Bachelor of Science, GNA University (2022). AI-training focus includes data types such as Text and labeling workflows including Fine-tuning.

Labeling Experience

Freelance AI Data Specialist

Data Annotation TechTextFine Tuning

As a Freelance AI Data Specialist for DataAnnotation.tech, I evaluated and trained Large Language Models to improve their response accuracy and reasoning capabilities. My work included fine-tuning bilingual AI models using diverse datasets in English, Hindi, and Hinglish. I directly contributed to LLM safety and content moderation practices to ensure responsible AI deployment. • Conducted quality evaluation and RLHF on text responses for LLMs. • Fine-tuned datasets involving multilingual data, focusing on code-mixing and translation accuracy. • Labeled and rated output for relevance, coherence, and appropriateness across languages. • Collaborated with team members to set up annotation guidelines and enhance model accuracy.

As a Freelance AI Data Specialist for DataAnnotation.tech, I evaluated and trained Large Language Models to improve their response accuracy and reasoning capabilities. My work included fine-tuning bilingual AI models using diverse datasets in English, Hindi, and Hinglish. I directly contributed to LLM safety and content moderation practices to ensure responsible AI deployment. • Conducted quality evaluation and RLHF on text responses for LLMs. • Fine-tuned datasets involving multilingual data, focusing on code-mixing and translation accuracy. • Labeled and rated output for relevance, coherence, and appropriateness across languages. • Collaborated with team members to set up annotation guidelines and enhance model accuracy.

2025 - Present

Education

Work History