Anas Kashif Qureshi

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Video

Top Label Types

Bounding Box

Text Generation

Evaluation Rating

Prompt Response Writing SFT

Audio Recording

Freelancer Overview

Highly motivated First-Year Mechatronics and Autonomous Systems Engineering student at Liverpool John Moores University with a strong foundation in Mathematics, Physics, and Data Science tools (Python, SQL) and a strong interest in automation, control systems, and robotics. Proven past practical experience as an AI Annotator – Generalist, validating AI products by labeling raw data, refining model prompts, and performing detailed quality analysis to ensure accurate, reliable model behavior. Technically proficient in Python, SQL, data visualization, and analytics, with exposure to machine learning and deep learning, and experience working with large datasets to generate actionable insights. Demonstrated leadership, communication, and teamwork skills through academic projects and responsibilities, with a clear motivation to contribute to multidisciplinary, fast-paced AI training environments focused on Human-in-the-Loop Model Evaluation, Data Quality, Prompt Iteration, Training Data Quality Assurance, Consistency, Edge-Case Handling, LLM Response Evaluation, Preference Judgments, Prompt-Based Tuning Support, Annotation QA, Guideline Adherence, Reliable Ground-Truth Creation, Model Output Review, Safety/Accuracy Checks, and Continuous Feedback Loops.

Labeling Experience

AI Annotator

Scale AIImageBounding BoxText Generation

My work includes Human-in-the-Loop Model Evaluation, LLM Response Evaluation, and Preference Judgments to compare, rate, and refine large language model outputs for usefulness, safety, and alignment with detailed project guidelines. I support Prompt Iteration and Prompt-Based Tuning by generating and improving prompts and responses (SFT), ensuring models learn from high‑quality, diverse examples. I perform Training Data Quality Assurance and Annotation QA, checking for Consistency, Guideline Adherence, and Reliable Ground-Truth Creation across labeling tasks such as Bounding Box annotation, Text Generation, Evaluation/Rating, Prompt + Response Writing, and Audio Recording. I also handle Edge-Case Handling and Model Output Review, performing Safety/Accuracy Checks and contributing to Continuous Feedback Loops that systematically improve both data pipelines and downstream AI model performance over time.

My work includes Human-in-the-Loop Model Evaluation, LLM Response Evaluation, and Preference Judgments to compare, rate, and refine large language model outputs for usefulness, safety, and alignment with detailed project guidelines. I support Prompt Iteration and Prompt-Based Tuning by generating and improving prompts and responses (SFT), ensuring models learn from high‑quality, diverse examples. I perform Training Data Quality Assurance and Annotation QA, checking for Consistency, Guideline Adherence, and Reliable Ground-Truth Creation across labeling tasks such as Bounding Box annotation, Text Generation, Evaluation/Rating, Prompt + Response Writing, and Audio Recording. I also handle Edge-Case Handling and Model Output Review, performing Safety/Accuracy Checks and contributing to Continuous Feedback Loops that systematically improve both data pipelines and downstream AI model performance over time.

2024 - 2025

Education

Work History