- 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.

- 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.