Nazim Ansari

Key Skills

Software

Telus

Doccano

Labelbox

Top Subject Matter

Mathematics Domain Expertise

Academic AI Training

Prompt Engineering

Top Data Types

Text

Computer Code Programming

Image

Top Task Types

Prompt Response Writing SFT

Entity Ner Classification

Question Answering

RLHF

Freelancer Overview

AI Trainer & Prompt Engineer, TELUS Digital AI Data Solutions, Turing, Mercor. Brings 9+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Telus, Doccano, and Labelbox. Education includes Doctor of Philosophy, Aligarh Muslim University (2024) and Master of Science, Aligarh Muslim University (2019). AI-training focus includes data types such as Text and labeling workflows including Prompt + Response Writing (SFT) and Entity (NER) Classification.

Labeling Experience

Mathematics Domain Expert

ImageQuestion Answering

Worked as a Mathematics Domain Expert on AI training and data labeling projects, focusing on question-answering tasks for image-based and text-based mathematical problems. The role involved creating, reviewing, and validating high-quality mathematical content across topics such as algebra, calculus, linear algebra, and graph theory. Performed annotation and evaluation of AI-generated responses, ensuring correctness, logical consistency, and step-by-step reasoning accuracy. Contributed to training datasets for large language models by designing complex, multi-step problems and identifying reasoning gaps in model outputs. Handled large-scale datasets with strict adherence to annotation guidelines, quality control standards, and review protocols. Maintained high accuracy through detailed validation, peer review, and continuous feedback cycles to improve dataset quality and model performance. This project strengthened expertise in AI evaluation, prompt engineering, and mathematical reasoning validation, contributing to the development of more reliable and accurate AI systems.

Worked as a Mathematics Domain Expert on AI training and data labeling projects, focusing on question-answering tasks for image-based and text-based mathematical problems. The role involved creating, reviewing, and validating high-quality mathematical content across topics such as algebra, calculus, linear algebra, and graph theory. Performed annotation and evaluation of AI-generated responses, ensuring correctness, logical consistency, and step-by-step reasoning accuracy. Contributed to training datasets for large language models by designing complex, multi-step problems and identifying reasoning gaps in model outputs. Handled large-scale datasets with strict adherence to annotation guidelines, quality control standards, and review protocols. Maintained high accuracy through detailed validation, peer review, and continuous feedback cycles to improve dataset quality and model performance. This project strengthened expertise in AI evaluation, prompt engineering, and mathematical reasoning validation, contributing to the development of more reliable and accurate AI systems.

2025 - Present

Mathematics Specialists

TextPrompt Response Writing SFT

My Role at Turing creating a challenging Prompts (Maths), Breaking the Advanced AI Models Like GPT-5, 5.2, 5.4, Cloud, Opus, Grok, Deepseek etc.

My Role at Turing creating a challenging Prompts (Maths), Breaking the Advanced AI Models Like GPT-5, 5.2, 5.4, Cloud, Opus, Grok, Deepseek etc.

2025 - Present

Data Annotator & Annotation Reviewer, Soul AI

DoccanoTextEntity Ner Classification

As a Data Annotator & Annotation Reviewer at Soul AI, I performed detailed data labeling, annotation, and validation for AI datasets. My responsibilities included reviewing annotations for accuracy in entity recognition and classification tasks and contributing to high-quality AI training pipelines. I designed and implemented a QA process to ensure the integrity of labeled data. • Conducted entity recognition and classification labeling on text datasets. • Implemented structured QA reviews for accuracy and consistency. • Provided guidance on annotation guidelines to improve label quality. • Used Doccano and Labelbox platforms for text-based data annotation and review.

As a Data Annotator & Annotation Reviewer at Soul AI, I performed detailed data labeling, annotation, and validation for AI datasets. My responsibilities included reviewing annotations for accuracy in entity recognition and classification tasks and contributing to high-quality AI training pipelines. I designed and implemented a QA process to ensure the integrity of labeled data. • Conducted entity recognition and classification labeling on text datasets. • Implemented structured QA reviews for accuracy and consistency. • Provided guidance on annotation guidelines to improve label quality. • Used Doccano and Labelbox platforms for text-based data annotation and review.

2025 - Present

AI Trainer & Prompt Engineer, TELUS Digital AI Data Solutions

TelusTextPrompt Response Writing SFT

As an AI Trainer and Prompt Engineer at TELUS Digital AI Data Solutions, I created and refined complex prompts for math-based problem solving and reasoning. My work involved training AI models and evaluating generated content for logical correctness. I ensured that AI output aligns with academic pedagogy and subject guidelines. • Generated and tested over 1000 domain-specific academic prompts for generative AI models. • Evaluated and rated model outputs for factual accuracy and adherence to task instructions. • Collaborated with subject matter experts to set annotation guidelines and quality standards. • Used proprietary tools and workflows to streamline labeling and evaluation processes.

As an AI Trainer and Prompt Engineer at TELUS Digital AI Data Solutions, I created and refined complex prompts for math-based problem solving and reasoning. My work involved training AI models and evaluating generated content for logical correctness. I ensured that AI output aligns with academic pedagogy and subject guidelines. • Generated and tested over 1000 domain-specific academic prompts for generative AI models. • Evaluated and rated model outputs for factual accuracy and adherence to task instructions. • Collaborated with subject matter experts to set annotation guidelines and quality standards. • Used proprietary tools and workflows to streamline labeling and evaluation processes.

2025 - Present

Education

Work History