Ismail OUGAMANE

Mission : Collaboration avec l’équipe business pour mener des projets de valorisation des données et de transformation digitale en intégrant des composants d’IA. • Développement et maintenance de processus ETL sur des plateformes cloud. • Conception et déploiement de pipelines de données sur Snowflake. • Intégration de modèles d’IA et de LLM pour l’enrichissement sémantique et l’analyse avancée des données. • Automatisation et orchestration des workflows avec des outils d’IA et de machine learning. Outils : Snowflake, LLM, IA, AWS (S3, EMR, RDS, Redshift, Lambda, Step Functions), Terraform, Python, Spark, GitLab.

- Developed and maintained ETL processes to centralize data for analysts and other teams. - Engineered cloud data pipelines and automated workflows. - Created and maintained a data warehouse in AWS Redshift, organizing data into raw, dim, and rep layers. - Developed a data catalog with AWS Glue. - Orchestrated workflows using VTOM. Tools: AWS (S3, EMR, RDS, Redshift, Glue, Lambda), Informatica, VTOM, Terraform, SFTP, Python, Spark, Git, GitLab.

Mission 1: Smart Factory Monitoring Platform | BP • Designed and implemented real-time data streaming solutions using AWS Kinesis, IoT Core, and Lambda, ensuring seamless capture and processing of sensor data from smart factory devices. • Developed data storage and management strategies by leveraging AWS S3 and IoT Sitewise, ensuring secure and efficient storage, along with the creation of a comprehensive data model for smart factory assets. • Automated data workflows through AWS Step Functions, orchestrating data pipelines and enabling smooth data movement and processing across multiple AWS services. Tools: Python, AWS CloudFormation, AWS kinesis, AWS Lambda, IoT CORE, AWS S3, IoT Sitewise, Git,Azure DevOps Server, AWS greengrass, AWS Step Functions. Mission 2: Solution Migration from Hanhaa to AWS | DS Smith • Conducted a comprehensive assessment of the existing Hanhaa solution, analyzing its architecture and dependencies, and identified areas for optimization during the migration process. • Led the development and execution of a successful migration strategy, leveraging AWS Lambda, S3, Git, and AWS CodeCommit to efficiently transfer and store data on the AWS platform. • Implemented a scalable and high-performance data warehouse on AWS Redshift, ensuring seamless data migration and transformation while maintaining data integrity for advanced analytics and reporting. Tools: Python, AWS Lambda, AWS S3, Git, AWS CodeCommit, AWS RedShift.

Mission 1: SCOR Group IT aims to reduce request processing time and improve customer experience. | Scor • Implement and refine NLP and ML models to extract and categorize information from request documents. • Collaborate with multidisciplinary teams to integrate the tool into existing systems and ensure smooth deployment. • Continuously evaluate and improve the tool’s performance by analyzing data and implementing necessary enhancements or updates. Tools: Python, NLU, NLTK, Flask, Pandas, Docker, Git, scikit-learn, Jenkins. Mission 2: SCOR Group IT aims to optimize the accounting process and reduce errors. | Scor • Develop and maintain the Data Capture Framework for classification and extraction of information from accounting documents. • Collaborate with accounting and finance teams to improve the accuracy and efficiency of the data extraction process. • Export the extracted information in an appropriate format for consumption by other services or systems. Tools: Python, NLU, NLTK, Flask, Pandas, numpy, Docker, Git, Jenkins

Mission 1: Develop and implement machine learning models for COVID-19 diagnosis. • Collect and preprocess data from COVID-19 patients. • Evaluate and refine model performance using relevant metrics. • Collaborate with domain experts to validate the accuracy and clinical relevance of the model. • Write conference paper, incorporating feedback and following submission guidelines. Tools: Python, Keras, Pandas, Flask, Git, scikit-learn Mission 2: Detection and classification of ocular diseases with Flask integration. • Collect and preprocess ocular imaging data, ensuring data quality and normalization. • Develop and fine-tune deep learning models for accurate detection and classification of ocular diseases. • Integrate the model into a Flask application, creating entry points for input images and output predictions. Tools: Python, Keras, Pandas, Flask, Git, scikit-learn

Study and implementation of a data management system

This study focuses on: - The security of the Wi-Fi network. - Comparison and choice of different solutions to put in place.

Realization of the Ping application (JAVA language).