About Alaa
ML Engineer depuis 4 ans au sein d'une start-up qui a réalisé un exit réussi, j'ai participé à la croissance de son équipe data et d’implémenter plusieurs services “from scratch”, en faisant preuve d’initiative, autonomie, et adaptabilité.
J’ai acquis et partagé des connaissances et best practices, que je continue à développer
aujourd’hui autour de projets data afin de maximiser la valeur ajoutée business.
French
Native or bilingual
English
Native or bilingual
Arabic
Native or bilingual
Spanish
Conversational
Can work on-site
Paris (up to 15km)
Experience
- DiliTrustSenior ML EngineerTECHMarch 2019 - March 2023 (4 years)Paris, France■ Contribute to defining mid / long term strategic ML objectives in close collaboration with the Product team■ Share best practices and processes■ Promote sustainable and scalable growth■ Build End to End NLP pipelines for legal document analysis, leveraging state of the art language modeling architectures■ Deploy services / workloads on a Kubernetes cluster using Terraform scripts (Infrastructure as a code)■ Contribute to code maintenance, testing and integration.■ Serve embeddings models using Flask / Gunicorn to speed uploading time during inference■ Extract, clean, export and maintain the ML Datalake (GCS)■ Export GCP Logs to a BigQuery Dataset, via Log Routers(terraform), and create Grafana dashboards for analysis■ Benchmarked and implemented SOTA contextualized textual embeddings■ Parallelized existing parts of code using native python and Dask■ Designed, trained and deployed a signature detection model
- Algorithmic crypto trading botML EngineerTECHJanuary 2021 - Today (5 years and 5 months)Paris, France■ Implemented and dockerized from scratch a crypto trading bot, including backtesting engine and exchange handler■ Implemented a real-time slack bot for remote control■ Implemented a reactive dashboard to evaluate / monitorstrategy performances
- ConfidentialFreelance ML EngineerRETAIL (SMALL BUSINESS)November 2019 - January 2020 (2 months)Paris, France■ Loaded and processed, in a distributed fashion, a large dataset of patents (500GB + in relational tables) into Parquet format■ Hierarchical Feature engineering■ Classified, in parallel, each patent by its IPC (international patentclassification) on a GCP
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Education
- Master of ScienceEURECOM2019Data Science, Machine/Deep Learning, Statistical inference, Image processing, Text mining, NLP, Big Data.
- Engineer's Degree, IT & CommunicationSUP'COM2018Engineer's Degree, IT & Communication