Welcome to my GitHub profile! I'm a highly motivated DevOps Engineer with a passion for leveraging technology to streamline development workflows and optimize system performance. Skilled in AWS, GCP, Azure, Docker, Kubernetes, Terraform, Python, Git & GitHub, CI/CD, and Linux, I'm committed to continuous learning and professional growth in the field of DevOps Engineering.
- πΌ Current Position: DevOps Engineer
- π Education: BSc. in Computer Science & Engineering (In Progress)
Explore more details about me:
- π± Learning: Currently engaged in a DevOps Engineering Career Track course, focusing on cloud computing, automation, CI/CD, and more. I'm also learning Docker, Kubernetes, Terraform, Golang, Azure, CI/CD, Security Monitoring & Best Practices.
- π― Collaboration: I'm looking to collaborate on innovative open-source projects that leverage cutting-edge technologies to solve real-world problems and make a positive impact on society. Whether it's developing scalable solutions, contributing to open-source initiatives, or exploring emerging trends, I'm eager to collaborate with like-minded individuals and teams to create something truly remarkable.
- π€ Seeking Help: I'm looking for help with advancing my skills and expertise in the field of DevOps. Specifically, I'm seeking opportunities where I can contribute to impactful projects, collaborate with talented individuals, and further develop my proficiency in cloud technologies, automation, and infrastructure management. Any guidance, mentorship, or advice from experienced professionals in the industry would be greatly appreciated as I continue to grow and excel in my career journey.
Here are some of my key projects that you might find interesting:
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Cloud-Migration-and-Management:
- Technologies: AWS, GCP, Azure, Terraform
- Challenge: Designing a scalable multi-cloud architecture
- Outcome: Successfully migrated infrastructure with 99.9% uptime and 30% cost reduction
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CI-CD-Pipeline-for-Infra-Deployment-Using-Terraform-GitHub-Action:
- Technologies: Terraform, GitHub Actions, AWS
- Challenge: Automating infrastructure deployment across multiple environments
- Outcome: Reduced deployment time by 70% and eliminated manual configuration errors
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CI-CD-Pipeline-for-Application-Deployment-Using-GitHub-Actions:
- Technologies: GitHub Actions, Docker, Kubernetes
- Challenge: Implementing a robust CI/CD pipeline for a microservices architecture
- Outcome: Achieved zero-downtime deployments and improved release frequency by 200%
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Layer-4-7-Load-Balancer-Deployment:
- Technologies: NGINX, HAProxy, AWS ELB
- Challenge: Optimizing traffic distribution for high-load applications
- Outcome: Improved application response times by 40% and increased system reliability
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- Technologies: Google Kubernetes Engine, tcpdump, Wireshark
- Challenge: Troubleshooting complex networking issues in a Kubernetes cluster
- Outcome: Resolved critical network bottlenecks, improving overall cluster performance by 25%
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Full-Stack-Application-Deployment-with-Docker:
- Technologies: Docker, Node.js, React, MongoDB
- Challenge: Containerizing a full-stack application for consistent deployments
- Outcome: Reduced environment setup time from hours to minutes, improving developer productivity
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Full-Stack-Application-Deployment-in-GCP / AWS-EC2 / AKS:
- Technologies: GCP, AWS, Azure, Kubernetes
- Challenge: Implementing a cloud-agnostic deployment strategy
- Outcome: Achieved 99.99% uptime across multiple cloud providers, enhancing disaster recovery capabilities
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Deployment-a-Full-Stack-Application-Infra-using-Terraform-in-AWS / GKE / AWS-Fargate / GCP / Azure:
- Technologies: Terraform, AWS, GCP, Azure, Kubernetes, Fargate
- Challenge: Implementing infrastructure as code across multiple cloud platforms
- Outcome: Reduced infrastructure provisioning time by 80% and improved consistency across environments
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DB-Backup-Retrieval-Across-Multiple-Servers:
- Technologies: Python, SQL, AWS S3, Cron
- Challenge: Designing a robust, automated backup system for distributed databases
- Outcome: Achieved 100% backup success rate and reduced data recovery time from hours to minutes
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- Technologies: Python, APIs, Data Processing Libraries
- Challenge: Automating complex workflows and data analysis tasks
- Outcome: Saved over 20 hours per week in manual tasks across various departments
Feel free to explore my repositories for more projects!
Explore my insights and industry updates
I regularly share my knowledge and experiences on various platforms. Here's where you can find my articles and posts:
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LinkedIn Articles: Professional insights, career advice, and industry trends in DevOps and cloud technologies.
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Dev.to Blog: In-depth technical tutorials, code snippets, and best practices for developers and DevOps engineers.
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Medium: Comprehensive guides on cloud architecture, automation strategies, and emerging technologies in IT.
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Personal Blog: Weekly updates on my projects, learning journey, and reflections on the evolving tech landscape.
Feel free to check out these platforms for a deeper dive into my thoughts and experiences in the world of DevOps and software engineering!
Feel free to ask me about anything related to:
- Python scripting and automation
- Cloud infrastructure management (AWS, GCP, Azure)
- Docker containerization and Kubernetes orchestration
- CI/CD pipeline design and implementation
- Linux administration and troubleshooting
- IT operations and best practices
Whether you're looking for advice, troubleshooting tips, or just want to chat about technology, I'm here to help. Let's geek out together!