Full-Stack Cloud Engineer passionate about building scalable, intelligent systems 🚀
I love architecting solutions that solve real problems - from AI-powered applications to production Kubernetes clusters. Always learning, always building, always sharing.
I'm fascinated by the intersection of cloud infrastructure, artificial intelligence, and developer experience. My repositories showcase this through practical projects that demonstrate different architectural patterns and problem-solving approaches.
Core Interests:
- 🤖 AI Integration: Building systems that leverage machine learning in practical, cost-effective ways
- ☁️ Cloud Architecture: Designing scalable, resilient infrastructure on AWS
- 🚢 Container Orchestration: Creating production-ready Kubernetes deployments
- 📊 Cost Optimization: Engineering solutions that scale efficiently without breaking budgets
- 🔧 Developer Experience: Building tools and systems that make other developers' lives easier
A serverless recommendation system that combines the Google Books API with intelligent caching to provide instant book suggestions.
What makes this interesting:
- Serverless Architecture: AWS Lambda + API Gateway + DynamoDB
- Smart Caching: Reduces API costs while maintaining real-time performance
- Full Automation: GitHub Actions CI/CD with Terraform Infrastructure as Code
- User Experience: Simple, fast interface with Tailwind CSS
Technical Highlights: Demonstrates event-driven architecture, API optimization, and cost-conscious cloud design.
An AI-powered pipeline that automatically extracts structured data from receipts and documents using computer vision.
What makes this interesting:
- Computer Vision: AWS Textract integration for intelligent text extraction
- Data Processing: Custom algorithms for structuring unorganized document data
- Scalable Design: Handles document processing at any volume
- Practical Application: Solves real business automation challenges
Technical Highlights: Shows integration of AI/ML services with custom business logic and scalable processing patterns.
A production-ready web application with intelligent auto-scaling and fault tolerance built on AWS.
What makes this interesting:
- High Availability: Multi-AZ deployment with automatic failover
- Auto-Scaling: Handles traffic spikes gracefully while optimizing costs
- Infrastructure as Code: Complete Terraform automation with GitLab CI/CD
- Production Patterns: Load balancing, health checks, and monitoring
Technical Highlights: Demonstrates enterprise-grade availability patterns and infrastructure automation.
A serverless notification system built with event-driven architecture principles.
What makes this interesting:
- Event-Driven Design: AWS SNS + Lambda for scalable message processing
- Serverless Economics: Pay-per-use pricing model that scales from zero
- Clean Architecture: Separation of concerns between frontend, API, and messaging
- Real-World Application: Powers notification systems for communities and organizations
Technical Highlights: Shows mastery of event-driven patterns and serverless architecture design.
A complete microservices platform running on AWS EKS with production-grade networking and security.
What makes this interesting:
- Microservices Architecture: Multiple services communicating through Kubernetes networking
- Production Infrastructure: AWS EKS + ALB + ECR with proper security configurations
- DevOps Integration: GitHub Actions CI/CD with automated deployments
- Real Debugging Experience: Documented solutions to complex networking and permissions challenges
- Comprehensive Documentation: Includes troubleshooting guides and lessons learned
Technical Highlights: Represents deep understanding of container orchestration, networking, and production operations.
Specialized Skills:
Cloud_Architecture:
AWS_Services: [EKS, ECS, EC2, Lambda, S3, DynamoDB, API_Gateway, ALB, VPC, IAM, SNS, CloudFront, Textract]
Infrastructure_as_Code: [Terraform, AWS_CLI, kubectl, Helm]
Container_Orchestration:
Kubernetes: [EKS_Clusters, Service_Mesh, Ingress_Controllers, Secrets_Management]
Docker: [Multi_stage_builds, Container_optimization, Registry_management]
AI_ML_Integration:
Computer_Vision: [AWS_Textract, Document_processing, Data_extraction]
API_Integration: [Google_Books_API, External_service_optimization, Caching_strategies]
Automation_DevOps:
CI_CD: [GitHub_Actions, GitLab_CI_CD, Automated_testing, Rolling_deployments]
Monitoring: [CloudWatch, Application_logging, Performance_metrics]My approach to learning is project-driven and problem-focused. Each repository represents not just a technical implementation, but a journey of understanding complex systems and architectural patterns.
Current Learning Areas:
- Advanced Kubernetes Patterns: Service mesh architectures and advanced networking
- FinOps Engineering: Cost optimization and resource management automation
- Infrastructure Security: Advanced security patterns and compliance automation
- Observability: Advanced monitoring and distributed system debugging techniques
Learning Philosophy:
"The best way to understand a technology is to solve real problems with it. Every project in my portfolio addresses a genuine challenge and demonstrates practical application of cloud-native principles."
I believe in learning in public and contributing to the developer community. My repositories include:
- Comprehensive Documentation: Each project includes setup guides, architecture explanations, and troubleshooting tips
- Real-World Examples: Code that solves actual problems, not just tutorial exercises
- Lessons Learned: Honest reflections on challenges faced and solutions discovered
- Open Source Mindset: Code designed to be understood, modified, and improved by others
Ways to Engage:
- 🌟 Star projects that inspire your own work
- 🐛 Open issues if you find bugs or have suggestions
- 💡 Fork and improve any projects that interest you
- 📝 Share feedback on architecture decisions or implementation approaches
I'm always excited to discuss cloud architecture, share learning experiences, and collaborate on interesting projects.
Great conversation topics:
- 🏗️ System Architecture: Design patterns for scalable cloud applications
- 🤖 AI Integration: Practical applications of machine learning in business systems
- 💰 Cost Optimization: Strategies for efficient cloud resource utilization
- 🔧 Developer Experience: Tools and practices that improve productivity
- 🚀 Career Growth: Learning paths and skill development in cloud engineering
Professional Growth: Seeking opportunities to apply my cloud architecture and AI integration skills in challenging environments where I can contribute to meaningful projects and continue learning from experienced teams.
Technical Interests: Exploring advanced patterns in microservices communication, infrastructure automation, and cost-effective scaling strategies.
Community Goals: Contributing to open source projects and sharing knowledge through documentation and technical writing.