For employers

Hire this AI Trainer

Sign in or create an account to invite AI Trainers to your job.

LoginCreate account
Invite to Job
Muhammad Abdullah

Muhammad Abdullah

AI Content Annotator & Fact-Checker (Independent/Project-Based)

PAKISTAN flag
Lahore, Pakistan
$5.00/hrExpertOtherAws SagemakerArgilla

Key Skills

Software

Other
AWS SageMakerAWS SageMaker
ArgillaArgilla
ClickworkerClickworker
Anno-MageAnno-Mage

Top Subject Matter

Large Language Models
Property Management
IoT Systems

Top Data Types

TextText
DocumentDocument
VideoVideo

Top Task Types

RLHF
Classification
Data Collection
Bounding Box
Polygon
Segmentation
Entity Ner Classification
Point Key Point
Polyline
Cuboid
Object Detection
Text Generation
Question Answering
Text Summarization
Fine Tuning
Red Teaming
Transcription
Evaluation Rating
Computer Programming Coding
Function Calling
Prompt Response Writing SFT

Freelancer Overview

AI Content Annotator & Fact-Checker (Independent/Project-Based). Brings 2+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Other. Education includes Bachelor of Science, FAST-NUCES Lahore (2024) and Intermediate, Government College University Lahore (2024). AI-training focus includes data types such as Text, Computer Code, and Programming and labeling workflows including RLHF, Computer Programming, and Coding.

ExpertEnglish

Labeling Experience

AI Output Evaluator and Tester

OtherText
I contributed to AI implementation projects by testing custom chat interfaces integrated with Gemini AI and DeepSeek models. My focus was on identifying and labeling hallucinations, conducting detailed evaluations of output responses. The work ensured quality and reliability of model-generated responses. • Tested and rated numerous AI outputs for factual accuracy • Labeled instances of hallucinations and errors for further model tuning • Documented evaluation procedures for future annotation standards • Played a crucial role in iterative model improvement cycles

I contributed to AI implementation projects by testing custom chat interfaces integrated with Gemini AI and DeepSeek models. My focus was on identifying and labeling hallucinations, conducting detailed evaluations of output responses. The work ensured quality and reliability of model-generated responses. • Tested and rated numerous AI outputs for factual accuracy • Labeled instances of hallucinations and errors for further model tuning • Documented evaluation procedures for future annotation standards • Played a crucial role in iterative model improvement cycles

Present

Code Annotator for AI Datasets

Other
In my code annotation projects, I worked extensively with datasets involving C++, JavaScript, and Python. I labeled each code block for critical features such as efficiency, readability, and potential security vulnerabilities. This labeling supported the training and evaluation of AI models on coding tasks. • Analyzed and annotated programming code for classification purposes • Focused on both code functionality and adherence to best practices • Ensured comprehensive security review in annotation process • Supported AI model training with high-quality code labels

In my code annotation projects, I worked extensively with datasets involving C++, JavaScript, and Python. I labeled each code block for critical features such as efficiency, readability, and potential security vulnerabilities. This labeling supported the training and evaluation of AI models on coding tasks. • Analyzed and annotated programming code for classification purposes • Focused on both code functionality and adherence to best practices • Ensured comprehensive security review in annotation process • Supported AI model training with high-quality code labels

Present

AI Content Annotator & Fact-Checker (Independent/Project-Based)

OtherTextRLHF
As an AI Content Annotator & Fact-Checker, I conducted comparative analysis of large language model responses, focusing on detecting logical fallacies and inaccuracies. I provided in-depth annotations and feedback for technical content, including code reviews and documentation validation. My work heavily emphasized ranking model outputs for accuracy, helpfulness, and safety. • Evaluated and compared AI-generated text outputs for reliability • Labeled code and technical content within multiple programming languages • Provided expert-level domain insights in property management and IoT systems • Ensured factual correctness and logical soundness in LLM outputs

As an AI Content Annotator & Fact-Checker, I conducted comparative analysis of large language model responses, focusing on detecting logical fallacies and inaccuracies. I provided in-depth annotations and feedback for technical content, including code reviews and documentation validation. My work heavily emphasized ranking model outputs for accuracy, helpfulness, and safety. • Evaluated and compared AI-generated text outputs for reliability • Labeled code and technical content within multiple programming languages • Provided expert-level domain insights in property management and IoT systems • Ensured factual correctness and logical soundness in LLM outputs

Present

Data Cleaner & Annotator (Ride-Sharing Project)

OtherTextData Collection
I performed extensive data cleaning and standardization tasks for ride-sharing architecture projects. My role involved organizing, labeling, and validating geographic and user text data to maintain quality datasets for AI applications. This effort supported subsequent model training and feature engineering. • Managed large-scale text data cleaning operations • Labeled fields critical for ride-sharing algorithms • Ensured data alignment for geospatial and user datasets • Facilitated robust dataset preparation for AI modeling

I performed extensive data cleaning and standardization tasks for ride-sharing architecture projects. My role involved organizing, labeling, and validating geographic and user text data to maintain quality datasets for AI applications. This effort supported subsequent model training and feature engineering. • Managed large-scale text data cleaning operations • Labeled fields critical for ride-sharing algorithms • Ensured data alignment for geospatial and user datasets • Facilitated robust dataset preparation for AI modeling

Not specified

Full Stack Developer Intern (AI Data Classification Focus)

OtherTextClassification
As a Full Stack Developer Intern with a focus on AI-driven workflows, I designed and helped label automated classification systems for property maintenance data. My work included building human-in-the-loop labeling logic and ensuring 98% classification accuracy. This process improved both automation and dataset quality for the property domain. • Created decision trees emulating annotation procedures • Oversaw AI validation processes within automated systems • Enhanced the accuracy of labeled property management data • Advanced the integration of AI APIs in smart property workflows

As a Full Stack Developer Intern with a focus on AI-driven workflows, I designed and helped label automated classification systems for property maintenance data. My work included building human-in-the-loop labeling logic and ensuring 98% classification accuracy. This process improved both automation and dataset quality for the property domain. • Created decision trees emulating annotation procedures • Oversaw AI validation processes within automated systems • Enhanced the accuracy of labeled property management data • Advanced the integration of AI APIs in smart property workflows

Not specified

Education

G

Government College University Lahore

Intermediate, Pre-Engineering

Intermediate
2022 - 2024
F

FAST-NUCES Lahore

Bachelor of Science, Software Engineering

Bachelor of Science
2024

Work History

Z

Zenolve

Full Stack Developer Intern

Lahore
2025 - Present
N

N/A

DevOps & Automation Engineer (Contract)

London
Not specified