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Hisham Malik

Hisham Malik

Agency

Multimodal Data Labeling & AI Training Expert

CANADA flag
Toronto, Canada
defined.ai/
$25.00/hrExpert1350+

Key Skills

Software

AppenAppen
CVATCVAT
LabelboxLabelbox
LabelImgLabelImg
V7 LabsV7 Labs
Surge AISurge AI
RoboflowRoboflow
EncordEncord
SuperviselySupervisely
ArgillaArgilla
SamaSama
HastyHasty
iMeritiMerit
TagtogTagtog
TolokaToloka
DoccanoDoccano
Data Annotation TechData Annotation Tech
DatatureDatature
OneFormaOneForma
MindriftMindrift
TelusTelus
MercorMercor
Internal/Proprietary Tooling

Top Subject Matter

Healthcare
Finance
E-commerce

Top Data Types

ImageImage
TextText
DocumentDocument

Top Task Types

Data Collection
Prompt Response Writing SFT
Object Detection
Question Answering
Text Summarization

Company Overview

DefinedAI is on a mission to enable the creators of the future. We believe AI should be created with the responsibility to make it the best version possible.

ExpertFrenchEnglish

Security

Security Overview

Defined.ai maintains a strong commitment to data security and privacy through industry-recognized standards and internal safeguards. The company is certified under ISO 27001, which ensures the implementation of a robust Information Security Management System (ISMS) to protect data confidentiality, integrity, and availability. Additionally, Defined.ai has obtained ISO 27701 certification, demonstrating its adherence to advanced privacy management practices and alignment with global regulations such as GDPR and CCPA.

Labeling Experience

LabelImg

E-commerce Product Categorization and Text Annotation

LabelimgTextClassificationText Generation
This project involved annotating large volumes of product descriptions and metadata to improve search relevance and recommendation systems in e-commerce platforms. Tasks included categorizing products, tagging key attributes, and identifying entities such as brand names, materials, and specifications. Annotators followed strict taxonomy guidelines and participated in ongoing calibration sessions to ensure consistency. Quality control included sampling audits and inter-annotator agreement tracking.

This project involved annotating large volumes of product descriptions and metadata to improve search relevance and recommendation systems in e-commerce platforms. Tasks included categorizing products, tagging key attributes, and identifying entities such as brand names, materials, and specifications. Annotators followed strict taxonomy guidelines and participated in ongoing calibration sessions to ensure consistency. Quality control included sampling audits and inter-annotator agreement tracking.

Not specified
OneForma

Multilingual Speech Recognition Dataset Labeling

OneformaAudioRLHFFunction Calling
This project focused on building high-quality multilingual speech datasets for training automatic speech recognition (ASR) systems. Annotators transcribed audio clips across multiple languages while ensuring accuracy in spelling, punctuation, and contextual meaning. The project included noise filtering, speaker differentiation, and timestamp alignment. Quality assurance processes included double-blind reviews and automated validation checks to maintain transcription accuracy above 95%.

This project focused on building high-quality multilingual speech datasets for training automatic speech recognition (ASR) systems. Annotators transcribed audio clips across multiple languages while ensuring accuracy in spelling, punctuation, and contextual meaning. The project included noise filtering, speaker differentiation, and timestamp alignment. Quality assurance processes included double-blind reviews and automated validation checks to maintain transcription accuracy above 95%.

Not specified
CVAT

Autonomous Driving Image Annotation Project

CVATImageBounding BoxSegmentation
This project involved large-scale annotation of street-level imagery to support the development of autonomous driving systems. The dataset included thousands of images containing vehicles, pedestrians, traffic signs, and road infrastructure. Annotators performed bounding box labeling and semantic segmentation to accurately identify and classify objects in complex urban environments. Strict quality control measures were implemented, including multi-pass review systems, consensus validation, and edge-case handling to ensure high precision and consistency across the dataset.

This project involved large-scale annotation of street-level imagery to support the development of autonomous driving systems. The dataset included thousands of images containing vehicles, pedestrians, traffic signs, and road infrastructure. Annotators performed bounding box labeling and semantic segmentation to accurately identify and classify objects in complex urban environments. Strict quality control measures were implemented, including multi-pass review systems, consensus validation, and edge-case handling to ensure high precision and consistency across the dataset.

Not specified