Aseef Ahmed

I have designed a scalable and secure continuous delivery architecture for a full-stack web application.

• The architecture is broken down into the relevant tiers (application, database, etc.).
• The architecture was provisioned via AWS CloudFormation.
• The deployment of new code and execution of tests was completely automated.
• The database and any mutable storage were backed up at least daily.
• All relevant logs for all tiers need to be easily accessible from a logging solution like Dynatrace.
• Implemented monitoring and logging using self-provisioned tools.
• The system can capture relevant historical metrics to spot and debug bottlenecks.

Launched a Python web application environment using Google Cloud Platform (GCP) app service. The app service provisions and manages the underlying infrastructure, such as GCP instances, and stack components, such as operating system, web server, and language/framework.

I deployed the web application using Google App Engine.

• Hosted the front-end application into S3 Bucket.
• Managed and authenticated the users through Amazon Cognito.
• Built the serverless back end with Lambda, NoSQL, and Python.
• Built and deployed RESTful APIs with API Gateway.

Used Ansible/chef to automate cloud infrastructure such as virtual machines, load balancer, storage, etc., in AWS/GCP.

Automated configuration management such as installing software packages, updating patches and configurations, etc., within the virtual machines.

Set up a Kinesis Agent on data sources to collect data and send it continuously to Amazon Kinesis Firehose. I also created an end-to-end data delivery stream using Kinesis Firehose. The delivery stream transmitted the data from the agent to destinations, including Amazon Kinesis Analytics, Amazon Redshift, Amazon Elasticsearch Service, and Amazon S3.

I processed incoming log data using SQL queries in Amazon Kinesis Analytics. I then loaded the processed data from Kinesis Analytics to Amazon Elasticsearch Service to index it, and I analyzed and visualized the processed data using Kibana.

Performed data analysis, cleaning, transformation with Pandas Library, and data visualization with Matplotlib and Tableau.

Implemented data ingestion pipeline using various AWS services like Lambda Function, Simple Storage Service (S3), Step Functions, etc.

Provisioned infrastructures in the following manner:
• Deployed three-tier web applications in Azure with Terraform.
• Deployed Terraform infrastructure using Azure DevOps.
• Deployed Terraform infrastructure in Azure using Jenkins.

• Configured build and release pipeline within Azure DevOps.
• Built infrastructure and deploying applications with Azure Pipelines.
• Monitored Kubernetes clusters using Prometheus and Grafana.
• Provisioned the infrastructure using Terraform.

• Implemented Azure Traffic Manager to minimize the application latency.
• Made sure Azure resources are highly available and fault-tolerant using various Azure offerings.
• Optimized cost while managing the resources with Azure.

• Deployed the Azure infrastructure using Terraform and Azure Pipeline.
• Used multiple deployment slots for development, staging, and production environment.
• Monitored the health check of the web application via Azure Log Analytics.
• Designed the infrastructure for deploying a three-tier application in multiple Azure R.

• Created Azure service principal.
• Configured and defined Ansible on Linux VM.
• Created Azure connections on Azure DevOps.
• Monitored different metrics via Azure native solution and other third-party log monitoring solutions like DataDog.

• Developed Terraform code for the infrastructure.
• Built the application using Azure Pipeline.
• Deployed Azure resources using Terraform (IaC) in an Azure CD pipeline.
• Automated infrastructure deployments in the cloud with Ansible and Azure Pi.

• Deployed a three-tier web application in Azure with Terraform.
• Deployed Terraform infrastructure using Azure DevOps.
• Deployed Terraform infrastructure in Azure using Jenkins.
• Optimized cost while managing the infrastructure.

With Kubernetes becoming the de facto standard for container orchestration, I build, test, and deploy code quickly to the Kubernetes cluster in GCP. Traditional continuous integration and continuous delivery (CI/CD) tools were not designed for cloud-native environments often fall short as developers spend many hours looking for best practices to automate deployments, scaling pipelines, and worrying about other implementation details.