Mike Elouardi

Key Skills

Software

AWS SageMaker

Appen

CVAT

Google Cloud Vertex AI

Img Lab

Labelbox

VoTT

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Label Types

Audio Recording

Classification

Data Collection

Object Detection

Text Summarization

Freelancer Overview

With a Master’s degree in Data Science and a background in Aerospace Engineering, I bring a unique combination of analytical expertise and engineering precision to AI training and data labeling projects. I have hands‑on experience with open‑source tools such as CVAT, LabelImg, and LabelMe, as well as cloud‑based platforms like SageMaker Ground Truth and Labelbox, enabling me to deliver high‑quality annotations across diverse domains. My expertise spans image classification, object detection, semantic segmentation, polygon annotation, and video object tracking, ensuring datasets are both accurate and production‑ready. I have contributed to projects involving computer vision, predictive modeling, and NLP, where precise training data was critical to model performance. My strengths lie in building scalable labeling pipelines, applying rigorous quality control, and aligning annotation strategies with downstream machine learning objectives. By combining technical depth with clear communication, I ensure that AI models trained on my datasets achieve measurable improvements in accuracy and reliability.

Labeling Experience

NLP Text Annotation for AI Training

LabelboxTextEntity Ner ClassificationClassification

Managed a large‑scale NLP annotation project involving technical documents and conversational datasets. Tasks included named entity recognition (NER) for extracting structured information (names, dates, organizations), text classification for categorizing documents by topic, and relationship classification to capture semantic links between entities. Additionally, I performed question‑answering annotation and text summarization labeling to support training of advanced LLMs. The project covered over 10,000 text samples, with rigorous quality assurance through multi‑review validation and inter‑annotator agreement. Deliverables ensured high‑quality datasets that significantly improved downstream model accuracy in information retrieval and conversational AI systems.

Managed a large‑scale NLP annotation project involving technical documents and conversational datasets. Tasks included named entity recognition (NER) for extracting structured information (names, dates, organizations), text classification for categorizing documents by topic, and relationship classification to capture semantic links between entities. Additionally, I performed question‑answering annotation and text summarization labeling to support training of advanced LLMs. The project covered over 10,000 text samples, with rigorous quality assurance through multi‑review validation and inter‑annotator agreement. Deliverables ensured high‑quality datasets that significantly improved downstream model accuracy in information retrieval and conversational AI systems.

2024

Aerospace Image Annotation for Object Detection

CVATVideoBounding BoxPolygon

Led a large‑scale annotation project involving satellite and aerial imagery to train computer vision models for aerospace applications. Tasks included drawing bounding boxes and polygons around aircraft, vehicles, and infrastructure, performing pixel‑level segmentation for land cover classification, and tracking moving objects across video frames. The dataset exceeded 50,000 images and video frames, with strict quality control measures such as multi‑review validation and inter‑annotator agreement checks. Deliverables ensured high precision and recall for downstream AI models used in predictive maintenance and autonomous navigation.

Led a large‑scale annotation project involving satellite and aerial imagery to train computer vision models for aerospace applications. Tasks included drawing bounding boxes and polygons around aircraft, vehicles, and infrastructure, performing pixel‑level segmentation for land cover classification, and tracking moving objects across video frames. The dataset exceeded 50,000 images and video frames, with strict quality control measures such as multi‑review validation and inter‑annotator agreement checks. Deliverables ensured high precision and recall for downstream AI models used in predictive maintenance and autonomous navigation.

2023 - 2024

Education

Work History