Aaftab V.

Key Skills

Software

Scale AI

Appen

Mindrift

Mercor

Other

Top Subject Matter

LLM response evaluation

data labeling for generative AI training (RLHF)

AI training data annotation for localized datasets (semantic labeling, NER-like tagging, intent classification, translation QA)

Top Data Types

Text

Computer Code Programming

Audio

Top Task Types

Bounding Box

Object Detection

Text Generation

Text Summarization

Question Answering

RLHF

Fine-tuning

Transcription

Evaluation/Rating

Computer Programming/Coding

Data Collection

Prompt + Response Writing (SFT)

Freelancer Overview

Scale AI, AI Evaluator / Data Labeller (Freelance). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Scale AI, N, and A. Education includes Diploma from George Brown College (2024) and Certificate, Conestoga College (2023). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Translation.

Labeling Experience

Scale AI — AI Evaluator / Data Labeler (Freelance)

Scale AIText

Provided multi-criteria evaluation and annotation of AI-generated responses, including linguistic clarity, factual accuracy, harmlessness, and rubric adherence. Audited and refined high-volume contributor-submitted datasets to remove noise and support gold-standard model training. Contributed to model training efforts using RLHF by generating detailed justifications aligned to selected answers. • Evaluated multi-turn model outputs for quality and safety • Performed category classification and preference ranking across outputs • Provided guidance and clarified edge cases in annotation instructions • Supported onboarding for international contributor teams

2024 - Present

RWS Group — Data Labeler / AI Training Specialist

Text

Performed high-volume semantic annotation and structured extraction for proprietary, localized AI models, covering entity extraction and intent classification. Reviewed and corrected machine-generated translations and text to ensure grammatical, contextual, and regional accuracy. Identified and documented inconsistencies, biases, and edge cases to improve dataset quality and training-cycle effectiveness. • Conducted semantic labeling, entity tagging, and intent classification • Performed translation and localization quality correction • Flagged biases, inconsistencies, and edge cases for iteration • Collaborated with data scientists and leads to refine labeling guidelines

2025 - 2026

Education

Work History