AI-Powered DevSecOps SaaS Pipeline

Payment Interface
Payment
Chat Room
Home Page
Code terraform for Jenkins server
Code terraform for Jenkins server
Code terraform for Jenkins server
Code terraform for Jenkins server
Plugins
Pipeline for provisioning infrastructure to create EKS clusters and worker nodes
ArgoCD Interface
SonarQube Interface
Pipeline Frontend
Pipeline Backend
SonarQube
Deployment of the app in the cluster
AlertManager
Interview Questions
Common Job Interview Questions
Badge
AI Quiz Generator

Inspiration

I noticed that many companies struggle to deploy AI-powered applications securely and at scale. Manual setups and fragile pipelines slow down progress and cause security risks. I wanted to build a fully automated, secure pipeline that combines the best DevSecOps and cloud practices to simplify deployment and management.

What it does

This project delivers a complete DevSecOps pipeline that automatically deploys a three-tier SaaS application on AWS. The app offers an intelligent interview system that adapts questions based on the candidate’s role, job description, and experience level, evaluating technical and behavioral skills and providing personalized feedback.

How I built it

I used Terraform to manage AWS infrastructure as code, Jenkins for automated CI/CD pipelines, Docker and Kubernetes (EKS) for containerization and orchestration, SonarQube and Trivy for code quality and security scans, ArgoCD for GitOps deployments, and Prometheus & Grafana for monitoring and observability.

Challenges I ran into

Configuring IAM permissions securely without slowing down the pipeline was tricky. Synchronizing ArgoCD with rapid code changes needed fine-tuning. Managing persistent storage for databases in Kubernetes was complex. Ensuring the system scaled well under load took several rounds of testing and optimization.

Accomplishments I'm proud of

I created a fully automated pipeline that integrates security checks at every stage, supports GitOps workflows, and delivers a real AI-driven interviewing app. The infrastructure is scalable and monitored with real-time dashboards, allowing quick issue detection and rollback.

What I learned

This project taught me how to design secure, scalable cloud architectures using Infrastructure as Code. I gained experience combining multiple DevSecOps tools seamlessly, implementing GitOps with ArgoCD, and building intelligent SaaS applications that solve real business needs.

What's next for AI-Powered DevSecOps SaaS Pipeline

I plan to improve the AI interview engine with natural language processing to better understand candidate responses. I also want to add support for more cloud providers, improve user experience, and open source parts of the pipeline to help other developers deploy secure AI apps faster.