Abduljelil A.

Key Skills

Software

Labelbox

Top Subject Matter

Conversational AI/NLP training data annotation (anonymized voice calls)

Multilingual conversational AI (customer service/chatbot) data annotation

Legal Services & Contract Review

Top Data Types

Audio

Text

Document

Top Task Types

Transcription

Emotion Recognition

Freelancer Overview

LivaAI — Data Annotation Specialist (Contract). Core strengths include Internal, Proprietary Tooling, and Labelbox. Education includes Higher National Diploma, Federal Polytechnic Ilaro (2024) and National Diploma, Federal Polytechnic Ilaro (2021). AI-training focus includes data types such as Audio and labeling workflows including Transcription and Emotion Recognition.

Labeling Experience

LivaAI — Data Annotation Specialist (Contract)

AudioTranscription

LivaAI (Contract) worked as a Data Annotation Specialist to label anonymized conversational audio for AI training projects using recorded calls. Responsibilities included tagging speaker intent, sentiment, and conversation type, and converting raw audio into structured model-ready training data. The role required strict adherence to privacy/confidentiality standards while performing quality checks against original recordings. • Listened to recorded calls and labeled components such as speaker intent, sentiment, and conversation type. • Tagged speech segments in LivaAI’s annotation interface to produce structured training data. • Performed data quality reviews by cross-checking labels with the source recordings. • Collaborated with QA/engineering to refine labeling criteria, document work, and suggest automation opportunities.

2024 - Present

Telus Digital — AI Data Annotator (Full-Time)

LabelboxAudioEmotion Recognition

TELUS Digital (Full-Time) served as an AI Data Annotator for a multilingual NLP project, transcribing and annotating voice and text data to support customer service and chatbot conversation models. The work included adding intent and sentiment tags and marking utterances, dialogue turns, and contextual cues for speech recognition training. Annotator quality was upheld through double-checks and peer review participation. • Transcribed and annotated voice/text data, labeling customer service calls and chatbot conversations with intent and sentiment tags. • Used Labelbox and in-house tools to mark utterances, dialogue turns, and contextual cues. • Conducted double-checks and participated in peer review to ensure annotation clarity and accuracy. • Collaborated with developers, linguists, and QA to clarify ambiguous cases and update annotation guidelines.

2022 - 2023

Education

Work History