Ali Baghdadi Email & Phone Number

Montreal, Quebec, Ca

- Leadership: Led a specialized cloud team for multiple clients, fine-tuning and enhancing existing cloud infrastructures. Designed and deployed new functionalities in line with customer business goals and their cloud environment.- Infrastructure as Code: Implemented Infrastructure as Code on Azure and AWS using Terraform, enhancing cost-effectiveness, operational efficiency, and risk mitigation. - Networking and Firewalls: Hands-on expertise in network design and deployment of diverse network models including distributed and centralized (Hub-and-Spoke) architectures. Configured UDR, firewalls (Azure FW, AWS network FW, Checkpoint), ExpressRoute, P2S, S2S VPNs, and leveraged Azure VWAN/AWS TGW.- Monitoring and Logging: Implemented comprehensive monitoring, alerting, and ticketing solutions using tools like Azure Monitor, Function/Logic App to automate cloud monitoring alerts in platforms such as ServiceNow, Jira Service Desk, and BMC Helix. - Infrastructure Patching: Proposed and implemented infrastructure patching solutions using Azure Update Manager, Automation Account, and server management tools like SUSE Manager. - Disaster Recovery and Backup: Orchestrated the design and implementation of robust disaster recovery plans, backup policies and restore tests leveraging advanced tools such as Azure RSV, Backup Center, and AWS backup.- Identity Management: Hands-on experience in configuring Single Sign-On (SSO) for diverse applications. Managed users and groups within Microsoft Entra ID. - Proactive Troubleshooting and Support: Troubleshooted issues proficiently across diverse domains, including network, storage, OS, compute services, and applications, delivering timely and effective resolutions to enterprise clients.- Business Impact: Prepared Current Situation Assessments (CSA) for potential customers on both Azure and AWS platforms, offering insights into existing infrastructure, performance benchmarks, and optimization opportunities.

- Azure Development: Successfully developed over 10 Azure-based applications/micro-services utilizing C# and Python. Deployed solutions on Azure services, including Function Apps, Web Apps, IOT Hub, and Storage Accounts, to aggregate real-time public transportation, weather, and sensors data.- Built new and improved existing RESTful APIs to perform CRUD operations on Azure SQL DB, improving data management capabilities.- Designed and implemented diverse ML pipelines to deploy machine learning models on Azure. Leveraged Azure Machine Learning Studio, Function App, Blob Storages, and more for efficient model deployment.- Conducted extensive research and development to identify optimal cloud solutions aligning with business regulations, encompassing both financial and performance considerations.- Produced detailed documentation and reports highlighting applications availability, functionality, and performance.- Azure DevOps: Created robust CI/CD pipelines on Azure DevOps for deploying microservices on Windows and Linux App Service Plans, demonstrating scalability to serve multiple clients.- Data engineering: Hands-on experience in designing Power BI dashboards for streaming and historical data using Power BI desktop, Power BI service, and Azure services such as IoT Hub, Service Bus, and Event Grid.

Montreal, Qc, Ca

- Contribute to six publications; one journal paper, one book chapter , and four conference papers.- Design and implement seven novel Shifted-Scaled-Dirichlet-based non-parametric clustering algorithms.- Develop machine learning models on hidden Markov model framework, hierarchical Dirichlet process framework, and hierarchical Pitman-Yor process framework. Work on finite and infinite mixture models.- Offer simultaneous parameter estimation and model selection (model complexity determination).- Work on different feature extraction and feature selection methods such as VGG16, PCA, SIFT and BOW.- Test introduced models by a variety of real-world applications such as GTSRB, texture clustering, spam email, activity recognition, and health care datasets. Achieve promising results compared with alternative approaches.- Offer traceable learning calculations, exact approximations, and assured convergence using the variational learning approach.