Designed, configured, and deployed Amazon Web Services (AWS) for a multitude of applications utilizing the AWS stack (Including EC2, ECS, EKS, VPC, Glacier, Route53, S3, RDS, Cloud Watch, Cloud Trail, WAF, SNS, and IAM), focusing on high-availability, fault tolerance, and auto-scaling. Created customized AWS Identity and Access Management (IAM) policies for various AWS resources to have required accessibility to required resources. Utilized Cloud Watch to monitor resources such as EC2, CPU memory, Amazon RDS, DB services, DynamoDB tables, and EBS volumes. Creating S3 buckets and managing policies for S3 buckets and utilized S3 buckets and Glacier for storage and backup on AWS. Design AWS Cloud Formation templates to create custom sized VPC, subnets, NAT to ensure successful deployment of Web applications and database templates Implemented a 'server less' architecture using API Gateway, Lambda, and Dynamo DB and deployed AWS Lambda code from Amazon S3 buckets. Created a Lambda Deployment function and configured it to receive events from your S3 bucket. Integrated services like GitHub, AWS Code Pipeline, Jenkins, and AWS Elastic Beanstalk to create a deployment pipeline Created an API Gateway to publish the data to AWS IoT core from the application using the HTTP request. Installed and configured HTTP Proxy Plug-ins on Legacy Apache Web Servers to send traffic to request dynamic content from WebLogic Application servers. Used Azure DevOps to drive all micro services builds out to the Docker registry and then deployed to Kubernetes, Created Pods and managed using Kubernetes.

Designed, configured, and deployed Amazon Web Services (AWS) for a multitude of applications utilizing the AWS stack (Including EC2, ECS, EKS, VPC, Glacier, Route53, S3, RDS, Cloud Watch, Cloud Trail, WAF, SNS, and IAM), focusing on high-availability, fault tolerance, and auto-scaling. Created customized AWS Identity and Access Management (IAM) policies for various AWS resources to have required accessibility to required resources. Created and configured AWS EC2 instances using preconfigured templates such as AMI, RHEL, Centos, and Ubuntu as well as used corporate-based VM images which includes complete packages to run build and test in those EC2 Instances. Configured an AWS Virtual Private Cloud (VPC) and database subnet group for isolation of resources within the Amazon RDS MySQL DB cluster. Implemented AWS High-Availability using AWS Elastic Load Balancing (ELB), which performed a balance across instances in multiple Availability Zones. Creating S3 buckets and managing policies for S3 buckets and utilized S3 buckets and Glacier for storage and backup on AWS. Database Migration from SQL Server to Amazon Redshift using AWS, worked on the AWS Data pipeline to configure data loads from S3 into Redshift. Automated AWS volumes snapshot backups for enterprise using Lambda. Integrated services like GitHub, AWS Code Pipeline, Jenkins, and AWS Elastic Beanstalk to create a deployment pipeline. Wrote TERRAFORM templates, Chef Cookbooks, recipes, and pushed them onto Chef Server for configuring EC2 Instances.

Prepared capacity and architecture plan to create the Azure Cloud environment to host migrated IaaS, VMs, and PaaS role instances for refactored applications and databases. Performed implementation of the Azure Operations dealing with IAAS infrastructure (Azure VMs, Virtual Networking, Azure services, Website Deployments) and deployed application as PaaS (Websites, Web Roles, and Worker Roles). Created Azure automated assets, Graphical runbooks, PowerShell run books that will automate specific tasks. Expertise in deploying Azure AD connect, configuring ADFS installation using Azure AD connect. Extracted and loaded data into Azure Blob Storage and Snowflake databases using Azure Data Factory and Data bricks. Implemented a CI/CD pipeline with Docker, Jenkins (TFS Plugin installed), Team Foundation Server (TFS), GitHub and Azure Container Service, whenever a new TFS/GitHub branch gets started, Jenkins, our Continuous Integration (CI) server, automatically attempts to build a new Docker container from it. Extract Transform and Load data from Sources Systems to Azure Data Storage services using a combination of Azure Data Factory, T-SQL, Spark SQL, and U-SQL Azure Data Lake Analytics. Managing secret keys through Azure key vault and configuring APIs to access the key vault through authentication process. Used Docker to virtualize deployment containers and push the code to instances cloud using PCF. Migration of on-premises data (Oracle/ SQL Server/ DB2/ MongoDB) to Azure Data Lake Store (ADLS) using Azure Data Factory (ADF V1/V2).