Cloud Engineering Projects

Implemented a highly efficient multi-branch CI/CD pipeline using Jenkins for a microservices-based application. The pipeline automatically triggered deployments to the development environment when code was pushed to the development branch, and upon pull request (PR) approval and merging into the main branch, Jenkins deployed to production.

The pipeline featured:

• Automated builds and tests for every branch push.
• Conditional deployments based on the branch—staging for dev branches and production for main.
• Integration with Terraform for provisioning AWS infrastructure.

This reduced deployment errors by 60%, and deployment times improved by 35%. Additionally, Jenkins' seamless integration with AWS EC2 and RDS helped manage cloud infrastructure efficiently.

Built a full-stack CI/CD pipeline using GitLab CI/CD for both frontend (React) and backend (Node.js) applications. We implemented distinct pipelines for the frontend and backend services, with automatic deployments to the staging environment on feature branch commits and production deployment post PR merge.

Key aspects:

• Docker containers were used for isolated environments during builds.
• Unit tests, integration tests, and linting were automatically triggered in the pipeline.
• Deployments to Vercel for frontend and AWS Lambda for backend (serverless architecture).

This improved developer productivity by 40%, reduced manual testing efforts by 70%, and saved hosting costs by utilizing serverless for the backend.

For a cloud-native serverless application, we employed GitHub Actions for CI/CD, ensuring that every code push resulted in automatic deployments to AWS Lambda using SAM (Serverless Application Model).

The architecture included:

• Code pushes to the development branch automatically triggered deployments to the dev environment for testing.
• Upon merging into the main branch, GitHub Actions deployed the updates to production Lambda functions.
• Unit tests and integration tests were run automatically within GitHub Actions workflows, ensuring smooth and error-free deployments.

This setup improved the release cycle by 50%, and the serverless architecture reduced infrastructure costs by 75% compared to traditional EC2 setups.

In this project, we leveraged AWS Fargate for managing containers and Amazon DocumentDB for database management in a cost-effective, highly scalable architecture for a fintech application.

Highlights include:

• Auto-scaling with Fargate reduced the need for manual instance management and led to a 30% savings in operational costs.
• Using Amazon DocumentDB for a fully managed NoSQL database ensured zero downtime and minimized latency.
• Infrastructure provisioning and management via Terraform, reducing human intervention and potential errors.

The project resulted in a 70% reduction in infrastructure costs by optimizing the compute, storage, and database layers.

Leveraged AWS Amplify to streamline the deployment of a frontend React application. The use of Amplify enabled continuous deployment, integration with GraphQL APIs, and fast rollbacks when needed.

Key results include:

• Instant deployment previews for every pull request, enabling faster feedback loops for the dev team.
• Serverless CI/CD pipeline with automatic testing and deployment of the React app upon merging to main branch.
• Easy integration with AWS services, reducing time spent on configuring separate services.

This project reduced deployment times by 30% and enhanced team collaboration, accelerating the development lifecycle by 25%.

Designed and implemented a multi-cloud deployment architecture across AWS, Azure, and Google Cloud to ensure high availability and disaster recovery. Kubernetes clusters were deployed using Helm and Terraform in multiple regions with seamless failover between clouds.

• Real-time failover across clouds to maintain 99.99% uptime.
• Cost savings by choosing the lowest-cost provider for each region.
• Automated scaling and disaster recovery processes using Kubernetes and Terraform.

This cross-cloud solution improved uptime while cutting costs by 20%, making it both resilient and efficient.

Executed a comprehensive lift and shift migration strategy for critical on-premises applications to AWS, ensuring minimal downtime and maximum cost efficiency. The migration encompassed the following:

• Assessment of existing applications for AWS compatibility and resource optimization.
• Use of AWS Migration Hub for tracking application migration progress and dependencies.
• Infrastructure as Code (IaC) implemented with Terraform for consistent provisioning of AWS resources, including EC2 instances, RDS databases, and S3 storage.
• Implementation of AWS Direct Connect for a secure and high-bandwidth connection between on-premises infrastructure and AWS, facilitating a smooth data transfer.
• Leveraged AWS Auto Scaling to adjust capacity based on demand, which significantly reduced costs by ensuring optimal resource allocation.

This migration resulted in a 40% reduction in operational costs, improved application performance, and enhanced scalability to support business growth.