About Mehdi
Ingénieur Data ayant évolué sur des problématiques de mise en production et de performance autour de volumes importants de données.
Passioné par les challenges techniques liés à la volumétrie de données, à la robustesse et la résilience, ainsi que par les challenges technico-organisationnels liés à l'architecture et à l'intégration de l'écosystème data au reste de l'entreprise.
French
Native or bilingual
English
Fluent
Arabic
Native or bilingual
Can work on-site
Paris (up to 50km)
Experience
- NIELSENIQDATA ENGINEERFebruary 2022 - Today (4 years and 4 months)Paris, FranceProject 1 - Semantic Search Engine :Build a search engine based on vector data basePrepare and clean Data.Upload data into the vector data base (Qdrant).Build API interface (FastAPI).Project 2 - Image Processing (ETL / ML Project):An end to end solution, it allows the clients to compare theauthenticity of images of their products published by their retailers.Database modeling (Cloud SQL).Build ETL pipeline (GCP Workflows, Cloud Run, Cloud SQL).Setup CI/CD pipeline (Bitbucket, Cloud Run, Cloud Build, Secrets).Unit Tests (Pytest, Tox)Build and Deploy ML modele (Kubernetes).Manage junior Data Engineers on the project.Project 3 - Promo ExtractionMachine learning (R&D) project that aim to extract relevant informations from promo textPrepare and clean Data.Compare ChatGPT, Gemini to propose relevant labels for NER models.Testing the recent model GLiNER.Project 4 - Product ProcessingBuild an ETL to process product data from scrapping spidersBuild ETL pipeline: MariaDB, Cloud Run, Polars, MongoDB, Airflow.Build APIs to provide data for different teams to get product data (Streamlit, Mongo).Setup CI/CD pipeline on Bitbucket.Technologies : Python, Django, Polars, MongoDB, Cloud SQL, SQL, CloudRun, CloudFunction, Docker,Kubernetes, Bitbucket, Tox, Streamlit, NoteBook, ChatGPT, Gemini, Qdrant, FastAPI
- DcubeData EngineerJune 2020 - February 2022 (1 year and 8 months)Paris, FranceProject 1 - Prediction of the number of sales per storeMission (POC) to present the Dataiku solution through a business use case for predicting the number of sales per storeLead the brainstorming session with the client.Build dashboards on Dataiku.Training a Linear Regression model.Project 2 - Data warehouse migration project to the Azure Cloud :Integration of new data flows and building ETLs pipelines on the Azure cloud.Define the pipeline and the different final users of the data.Build data flow on Azure.Upload data from on-premises servers to the Azure DataLake.Create CI/CD pipelines on Azure DevOps.Technologies : Python, Azure Datalake Gen2, Azure DevOps, Windows, Dataiku, Azure, Python, SQL
- Constant oatsINVENTIV ITJanuary 2019 - May 2020 (1 year and 4 months)Project 1 - Job Matching Platform :A solution built from scratch Allows recruiters to matchs the received CVs with the job offer descriptionImplement a CV recommendation system, Data collection (open API, CVs, HR database,Scrapping)Design of an ETL in batch mode.Implement and deploy the TF-IDF modelProject 2 - Named Entities ExtractionEnsure a better user experience through the development of a Deep Learning model for the extraction of named entitiesCollect new datasets.Build Machine learning models.Technologies : Python, Pandas, Sk-learn, Postgres, Flask, Lambda Function, S3, AWS, Python, Azure Datalake Gen2, Azure DevOps
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Education
- Master of International StudiesUniversite esn Monnet de S-Etenne,2019MASTER INTERNATIONAL MACHINE LEARNING ET DATA MINING
- DIPLOME D'INGENIEUR INFORMATIQUE LASSES PREPARATOIREeda2013DIPLOME D'INGENIEUR INFORMATIQUE LASSES PREPARATOIRE