Arabic NLP & service engineering
Lableb
Aug 2020 - Sep 2023AI Engineer
Remote
Arabic language, practical systems.
At Lableb, I worked on Arabic NLP and search-related B2B systems, moving between model research, comparative experiments, data preparation, and service development. I first joined as an AI Engineer Intern in April-July 2020, then started the full-time role in August.
Up to 30,000 requests/day
Filtering spam in a production service
I built a B2B spam-classification service handling up to 30,000 requests per day and detecting/filtering about 90% of spam queries. The work connected classification models with production service engineering, with attention to minimizing false positives on legitimate queries.
The 90% figure describes spam filtering. It is not established as overall accuracy, precision, or F1; no measured false-positive rate, customer count, or service-level objective is supplied.
Research & engineering
Comparing approaches to Arabic spelling
I compared NLTK n-gram models and neural noisy-channel approaches through iterative spelling-correction experiments. Moving to KenLM improved runtime with comparable results. DVC versioned related datasets and model artifacts.
I researched, trained, and evaluated Arabic named entity recognition, part-of-speech tagging, transliteration, and spelling correction in Azure Machine Learning, using task-appropriate F1, precision, recall, and accuracy. These research activities are distinct from the production spam service.
NLP microservices
Serving language capabilities
I built microservices for synonym extraction, spam filtering, English-to-Arabic transliteration, and converting unstructured text to structured data. Service work spanned Python/Flask, Go/Fiber, and Java/Spring Boot.
Data preparation and analysis used pandas, NumPy, Matplotlib, and Seaborn. I worked with other engineers on model and service delivery; the record does not assign every framework to a particular service or establish throughput for every NLP component.