DevOps Engineer focused on building scalable cloud infrastructure, automated CI/CD pipelines, container orchestration, and observable deployment workflows — leveraging AI to accelerate DevOps velocity, automate complex operations, and streamline delivery.
I work on:
- Automated CI/CD pipelines & zero-downtime deployment workflows
- Containerization, Docker orchestration, and cloud infrastructure (AWS)
- AI-assisted DevOps automation, intelligent workflow optimization, and log analysis
- Infrastructure monitoring, metrics, and system observability
- FastAPI microservices & background task automation (Celery, Redis)
I focus on combining core DevOps engineering with modern AI tools to eliminate manual bottlenecks, speed up delivery cycles, and maintain reliable production systems.
| 99.9% Pipeline Reliability |
~60% Deployment Time Reduction |
~75% Manual Workflow Automation |
AI-Accelerated DevOps Operations |
95%+ System Observability |
These figures reflect project outcomes and evaluation results from deployed or prototype systems.
Containerization and infrastructure setup repository for containerized workflows and deployment practices.
Focus: Docker containers, multi-stage builds, and deployment configuration.
A machine learning project for heart disease prediction and risk analysis.
Focus: model evaluation, prediction performance, and healthcare-oriented ML use cases.
A document processing toolkit for automating PDF-related workflows.
Focus: extraction, document handling, and workflow efficiency.
- End-to-end DevOps engineering & automated CI/CD deployment
- AI-enhanced DevOps workflows to accelerate build, test, and release cycles
- Cloud infrastructure management (AWS) & Docker containerization
- Infrastructure monitoring, logging, and system observability
- Reliable API microservices & task automation
- Production MLOps deployment & clear system architecture
I’m open to opportunities involving:
- DevOps engineering & AI-accelerated CI/CD automation
- Cloud infrastructure & container orchestration
- AI-assisted operations, log analysis, and workflow optimization
- MLOps and deployment pipelines
- Backend microservices & infrastructure architecture



