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Schedule Interview NowMy name is Farrukh S. and I have over 7 years years of experience in the tech industry. I specialize in the following technologies: node.js, React, Python, DevOps, JavaScript, etc.. I hold a degree in Bachelor of Applied Science (BASc). Some of the notable projects Iโve worked on include: Deployment Automation of Kubernetes Using GitLab CI/CD, Deployment Automation of Kubernetes Using GitLab CI/CD, Setting Up AWS CloudWatch to Monitor EC2 Instances & Lambda Functions, Setting Up GitHub Actions to Deploy Microservices - Azure AKS, Configuring GitLab CI/CD Pipeline for AKS Deployment, etc.. I am based in Sacramento, United States. I've successfully completed 15 projects while developing at Softaims.
I value a collaborative environment where shared knowledge leads to superior outcomes. I actively mentor junior team members, conduct thorough quality reviews, and champion engineering best practices across the team. I believe that the quality of the final product is a direct reflection of the team's cohesion and skill.
My experience at Softaims has refined my ability to effectively communicate complex technical concepts to non-technical stakeholders, ensuring project alignment from the outset. I am a strong believer in transparent processes and iterative delivery.
My main objective is to foster a culture of quality and accountability. I am motivated to contribute my expertise to projects that require not just technical skill, but also strong organizational and leadership abilities to succeed.
Main technologies
7 years
2 Years
6 Years
5 Years
Potentially possible
๐๐ผ๐ฎ๐น: Automate the deployment process for a Kubernetes application to improve speed, consistency, and scalability while reducing manual errors. ๐ฆ๐ผ๐น๐๐๐ถ๐ผ๐ป: Designed and implemented a GitLab CI/CD pipeline with Docker, Helm, and Kubernetes. Configured automated workflows for build, test, and deployment, integrated secrets management, and implemented monitoring with Prometheus and Grafana for real-time insights. ๐๐บ๐ฝ๐ฎ๐ฐ๐: Deployment time reduced by 75%, bugs in production decreased by 40%, and developer productivity improved by 50%.
๐๐ผ๐ฎ๐น: Automate the deployment process for a Kubernetes application to improve speed, consistency, and scalability while reducing manual errors. ๐ฆ๐ผ๐น๐๐๐ถ๐ผ๐ป: Designed and implemented a GitLab CI/CD pipeline with Docker, Helm, and Kubernetes. Configured automated workflows for build, test, and deployment, integrated secrets management, and implemented monitoring with Prometheus and Grafana for real-time insights. ๐๐บ๐ฝ๐ฎ๐ฐ๐: Deployment time reduced by 75%, bugs in production decreased by 40%, and developer productivity improved by 50%.
๐๐ผ๐ฎ๐น: Implement AWS CloudWatch for centralized monitoring of EC2 instances and Lambda functions to improve reliability, optimize resources, and address performance issues proactively. ๐๐ต๐ฎ๐น๐น๐ฒ๐ป๐ด๐ฒ: The client faced limited visibility, missed alerts, and inconsistent monitoring as their infrastructure scaled. ๐ฆ๐ผ๐น๐๐๐ถ๐ผ๐ป: Enabled detailed monitoring with metrics, logs, and alarms for EC2 and Lambda. Built unified dashboards for real-time insights, automated configurations with CloudFormation, and added SNS alerts and remediation workflows to ensure proactive issue resolution.
๐๐ผ๐ฎ๐น: Automate microservices deployment to Azure AKS using GitHub Actions for efficient CI/CD workflows. ๐๐ต๐ฎ๐น๐น๐ฒ๐ป๐ด๐ฒ: The client faced manual, error-prone deployments, inconsistent pipelines, and downtime risks. ๐ฆ๐ผ๐น๐๐๐ถ๐ผ๐ป: - Configured GitHub Actions to automate builds, tests, and deployments triggered on git push. - Used Azure service principals and GitHub Secrets for secure AKS deployments. - Implemented unit tests, pre-deployment checks, and blue-green rollouts for zero-downtime updates. - Added notifications and logs monitoring for real-time performance tracking.
A GitLab CI/CD pipeline was designed and implemented for automatically deploying a containerized microservices application to Azure Kubernetes Service (AKS). The pipeline took care to automate Docker buildup, testing, and all stereotypical deployment stages for Kubernetes orchestration through Helm. The system interfaced Azure Container Registry (ACR) to AKS to have proper image management. It brought Kubernetes auto-scaling to the forefront for scalability purposes and provided real-time monitoring using Grafana and Prometheus. The deployment time itself was reduced to 60%.
Bachelor of Applied Science (BASc) in Computer science
2005-01-01-2009-01-01