Hiba Achir

Key Skills

Software

Data Annotation Tech

Label Studio

Other

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Text

Top Label Types

Fine Tuning

Computer Programming Coding

Data Collection

Prompt Response Writing SFT

Freelancer Overview

I am an AI and Data Engineer with hands-on experience in preparing, annotating, and managing large-scale datasets for machine learning, NLP, and computer vision projects. My work includes structuring national-scale data for platforms like NearMe, extracting and transcribing audio from educational videos for conversational AI, and building intelligent systems that rely on high-quality, well-labeled training data such as chatbots, fraud detection models, and medical image classifiers. I am skilled in Python, Power BI, SQL, and a variety of AI frameworks (TensorFlow, PyTorch, Scikit-learn), and I have a strong understanding of data pipelines, ETL processes, and cloud technologies. My projects reflect a commitment to data quality, ethical AI, and delivering robust solutions across domains like healthcare, education, and e-commerce.

Labeling Experience

Image Data Labeling

OtherImageBounding BoxClassification

I worked on an image data labeling project focused on preparing high-quality datasets for computer vision models. My responsibilities included annotating images using bounding boxes and classification labels to identify objects and visual patterns relevant to the model’s objectives. The dataset consisted of a large volume of images requiring consistent and accurate labeling according to predefined guidelines. I followed strict quality standards by verifying annotations, correcting inconsistencies, and ensuring label accuracy to support effective model training. This project contributed to improving object detection and classification performance while maintaining data integrity and ethical AI practices.

I worked on an image data labeling project focused on preparing high-quality datasets for computer vision models. My responsibilities included annotating images using bounding boxes and classification labels to identify objects and visual patterns relevant to the model’s objectives. The dataset consisted of a large volume of images requiring consistent and accurate labeling according to predefined guidelines. I followed strict quality standards by verifying annotations, correcting inconsistencies, and ensuring label accuracy to support effective model training. This project contributed to improving object detection and classification performance while maintaining data integrity and ethical AI practices.

2024

Studio Voice Actor

Label StudioAudioAudio RecordingTranscription

This ongoing project focuses on high-quality audio data creation and annotation for AI training purposes. I work as a studio voice actor and audio contributor, producing clean, well-structured voice recordings used for speech recognition, text-to-speech (TTS), and conversational AI models. The scope includes recording scripted and semi-scripted audio, ensuring clarity, correct pronunciation, emotional consistency, and adherence to predefined linguistic and acoustic guidelines. In addition to audio recording, I perform transcription and validation tasks, aligning audio with accurate text labels and metadata. Quality control measures include noise reduction, consistency checks, and manual review to ensure datasets meet model training requirements. The annotated audio data supports machine learning workflows such as fine-tuning speech models, emotion recognition, and AI assistant training, with a strong focus on data accuracy, ethical use, and reproducibility.

This ongoing project focuses on high-quality audio data creation and annotation for AI training purposes. I work as a studio voice actor and audio contributor, producing clean, well-structured voice recordings used for speech recognition, text-to-speech (TTS), and conversational AI models. The scope includes recording scripted and semi-scripted audio, ensuring clarity, correct pronunciation, emotional consistency, and adherence to predefined linguistic and acoustic guidelines. In addition to audio recording, I perform transcription and validation tasks, aligning audio with accurate text labels and metadata. Quality control measures include noise reduction, consistency checks, and manual review to ensure datasets meet model training requirements. The annotated audio data supports machine learning workflows such as fine-tuning speech models, emotion recognition, and AI assistant training, with a strong focus on data accuracy, ethical use, and reproducibility.

2023

ai training

OtherTextQuestion AnsweringFine Tuning

AI TRAINING

AI TRAINING

2024 - 2025

Education

Work History