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Ismael El AtifiIE

Ismael El Atifi

Senior Machine Learning Engineer (Python)

840 €/jour
Paris, FR
8-15 ans

Délai de réponse moyen : 1h

À propos de Ismael

I have 10 years of professional experience, mainly in Data Science (Machine/Deep Learning) and more recently in Software/Data Engineering.

I like Machine/Deep Learning, algorithms, and efficient, readable and well structured code.
I have experience building predictive models but also deploying them in production, with proper CI/CD.

I am looking for a Senior Machine Learning Engineer mission.

The main technos I have used:
- Python
- SQL
- Pandas
- ScikitLearn
- xgboost / lightGBM / catboost
- PyTorch
- Tensorflow
- OpenCV
- Streamlit
- Plotly
- PySpark
- Databricks
- Airflow
- MLflow
- AWS
- Docker
- FastAPI
- GitHub Actions
- Terraform
  • Français

    Bilingue ou natif

  • Anglais

    Capacité professionnelle complète

Accepte de travailler sur site
Paris (jusqu’à 15 km)

Expériences

  • Mirakl
    Data Engineer
    novembre 2023 - janvier 2025 (1 an et 2 mois)
    Paris, France
    Python development around ML based ads recommendation. Implemented a backend with FastAPI responsible for model deployment with Databricks Model Serving, and allowing multiple models to be served atthe same time. Also developed the A/B testing dashboard with Streamlit to compare the CTR of multiple models, number of displays and other metrics.
  • ALLIANZ TRADE
    Software Engineer (Python, AWS)
    août 2022 - novembre 2023 (1 an et 3 mois)
    Paris, France
    Doing software development in a Scrum team of 4 davs. We develop and maintain Python backends to create and stream business objects by watching for changes in PostgreSQL databases. All our code runs on AWS (Lambda, ECS, Step functions, SQS, Glue PySpark,DynamoDB, PostgreSQL, Kinesis, DMS). We use Gitlab for CI/CD.
  • Pernod Ricard
    Data Scientist - ML
    novembre 2021 - juillet 2022 (8 mois)
    Paris, France
    Worked on optimization of promotional calendars (Python codebase in production). Future volumes sold for thousands of candidate promotional events are predicted with Gradient Boosted Trees (XGBoost), and then Mixed Integer optimization (PyOMO/Gurobi) is used to find the optimal subset of events which maximizes ROI given business constraints.
    Groupe Crédit Agricole

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Formations

  • Ingénieur ENSIMAG
    Grenoble INP - ENSIMAG
    2014
    Informatique et mathématiques appliquées

Compétences

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