Freelancer profile translated to English.
Back to original languageAbout Imed
Hello!
I am Imed, Cloud Architect âïž, expert in Azure and in designing robust and scalable architectures. With expertise in Terraform, DevOps, FinOps & GreenOps, I help companies design, optimize, and secure their cloud infrastructures.
Also, passionate about AI and Machine Learning, I design GenAI & ML architectures by integrating Azure AI Foundry, Azure ML, Databricks & Kubernetes đ.
My goal: industrialize, automate, and make your systems reliable for optimal scalability and cost control.
Need a cloud expert to accelerate your digital transformation? Let's discuss!
French
Native or bilingual
English
Native or bilingual
Arabic
Native or bilingual
Can work on-site
Paris (up to 50km)
Experience
- TotalEnergiesDevOps & Cloud GenAI Architect â AI/ML Innovation AcceleratorMay 2025 - Today (1 year and 1 month)Paris, FranceIn my current role at TotalEnergies, I operate at the intersection of DevOps, Cloud Architecture, and Generative Artificial Intelligence (GenAI). My role is to industrialize and secure multi-cloud AI/GenAI platforms (Azure and AWS), with a strong emphasis on CI/CD, Infrastructure as Code (IaC), security, and monitoring. My technical contributions:GenAI & LLM Hub Projects
- Multi-Cloud Architecture & Deployment: Design and deployment of a unified platform for accessing Large Language Models (LLMs) (OpenAI, Mistral, Claude, Bedrock). Infrastructure provisioned via Terraform (IaC) on Azure Container Apps (ACA), Azure Function Apps, Azure AI Services, Azure Kubernetes Service (AKS), AWS Lambda. Management of key Cloud resources: Resource Groups, VNET, Subnets, Key Vault, Storage Accounts, Azure SQL, EventHub, Application Insights.
- API Security & Governance: Implementation of a centralized API Manager with Private Endpoints, NSG, RBAC, and identity management (Managed Identities) via Azure Key Vault (versioning, secrets).
- FinOps & Optimization: Implementation of consumption monitoring and cost optimization solutions (e.g., Provisioned Throughput for LLMs).
- Secure API Exposure: Development of Azure Functions to deliver SAS tokens via Key Vault. Integration with APIGEE for API management.
- GenAI Challenges: Securing the provision of data (via SAS tokens) for training/usage of GenAI models and controlled API exposure.
Machine Learning Model Governance- Deployment, maintenance, and traceability via Terraform
- Azure Service Principals & AWS ARNs Configuration
- Backend for data extraction on Azure ML, Databricks
- GitHub Actions CI/CD Pipelines (deployment, secrets, env)
- Automation, Terraform factorization, alerting, Cloud lifecycle management
- CHANELCloud GenAI ArchitectJanuary 2024 - May 2025 (1 year and 4 months)Neuilly-sur-Seine, FranceAs a Cloud & GenAI Architect, I drive CHANEL's Cloud Innovation, orchestrating Dev/Data/AI synergy. I support the design and industrialization of GenAI architectures on Azure, while optimizing the security, scalability, and eco-efficiency of infrastructures. Here are some actions implemented:đ Cloud Leadership & Synergy
- Coordination of the Cloud Innovation team
- Alignment of Dev/Data/Cloud and dissemination of best practices
đ§ AI & GenAI Architectures on Azure- Design and deployment of GenAI and AI solutions
- Integration of Azure AI Foundry, AI Search, Machine Learning & Databricks
- Definition of the GenAI Blueprint: Infrastructure, Data, AI, Security, Integration
đ° FinOps & GreenOps- Cost and eco-efficiency optimization of Azure infrastructures
đ Security & Governance- Security by Design with Azure Security Center & Defender for Cloud
- Identity and access management with Entra ID
đ AI Deployment & Scalability- Industrialization of models with AML, Azure Functions & AKS
- Devoteam M CloudCloud Solutions ArchitectSeptember 2022 - January 2024 (1 year and 4 months)Levallois-Perret, FranceAs a Cloud Architect, I joined a technical team tasked with assisting developers and AI engineers in designing and industrializing their Gen AI & ML projects on Azure. My role is to create the concept of an end-to-end GenAI platform on Azure, structuring the architecture around scalable and secure cloud services. We integrated Azure AI Search and OpenAI Service to optimize model management and improve performance via RAG. The platform relies on a vector database (Cosmos MongoVCore) optimized for knowledge management and advanced monitoring with Azure Monitor and App Insights. The infrastructure was industrialized with Terraform and Kubernetes, ensuring scalability, performance, and security for AI use cases.đč Key missions:
- Definition & deployment of scalable architectures for Gen AI and ML projects on Azure
- Industrialization of AI workflows with Kubernetes, Azure Machine Learning, and Terraform
- Optimization of AI workload performance and costs with FinOps & GreenOps
- Implementation of CI/CD pipelines integrating DevOps best practices for AI model deployment and management
- Securing cloud environments with Azure Security Center, Defender for Cloud, and Entra ID
- Data governance and management with Azure Databricks & Unity Catalog
- Deployment of backend services in serverless architectures via Azure Functions & Web Apps
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Education
- Master 2 Networks and TelecommunicationsPierre and Marie Curie University Paris 6 and Technical University of Berlin2017
- Master 2 Double Degree: Digital Innovation & EntrepreneurshipEIT2017
Certifications
- Azure Solutions Architect ExpertPearson VUE2021
- Azure Administrator Associate (Legacy)Pearson VUE2019