1. Creation and use of GKE Autopilot clusters, virtual machines (VMs), Cloud Functions, Cloud Scheduler, Cloud Run, Cloud Build, and
   VPC on the GCP platform to automate test and production environments.
2. Implementation of CI/CD pipelines using GitHub Actions to ensure continuous integration and deployment on GKE and VMs.
3. Design of multi-stage Docker images to optimize image size and implementation of best security practices through the use of a nonroot
   user.
4. Use of Docker Compose for simultaneous deployment of multiple containers on VMs with volumes attached and a dedicated
   network.
5. Deployment of containers on Cloud Run using generated artifacts.
6. Management of lifecycle policies on Cloud Storage to reduce storage costs for the client.
7. Creation of Kubernetes Pods for Deployments, StatefulSets, ReplicaSets, CronPods, Secrets, Services, and RBAC, all orchestrated
   via YAML files and automatically deployed to GKE using GitHub Actions.
8. Setup of pod auto-scaling using VPA (Vertical Pod Autoscaler) and HPA (Horizontal Pod Autoscaler).
9. Integration of SonarQube to detect vulnerabilities in source code.
10. Creation of GKE infrastructure with Terraform, then integration with Ansible for pod deployment on the cluster as part of a PaaS
    solution.
11. Development of Bash scripts to automate backups and integrate them with Cloud Storage, following defined lifecycle rules.
12. Implementation of log monitoring, VPC firewall rules, and management of roles, service accounts, and permissions via IAM.
    13.Worked with a dedicated Hetzner server and created an OpenVPN configuration to connect to the remote server via SSH.
13. Created bash scripts to automate the server setup with the openVPN setup, Firewall rules and cron jobs.

The theme of the project is infrastructure migration to the cloud.

1. Implemented Amazon RDS Multi-AZ for automatic failover and high availability at the database level.
   2.Worked on Amazon RDS databases and executed functions to create instances as needed.
2. Integrated Amazon CloudWatch with Amazon EC2 instances for log monitoring and metric tracking.
3. Experienced with S3 versioning and lifecycle policies for backup and archival files in Glacier.
4. Created monitors, alarms, and notifications for EC2 hosts using CloudWatch; created S3 buckets, policies, and role-based IAM
   policies.
5. Configured AWS Identity and Access Management (IAM) groups and users to enhance login authentication

Project Theme: Data Migration to the Cloud.

1. Use of cloud technologies such as Snowflake Cloud, Informatica, and Azure DevOps.
2. Implementation of data pipelines using Informatica.
3. Management of CI/CD pipelines for an insurance project with AWS DevOps.
4. Hands-on experience with Jenkins for CI/CD processes.
5. Creation of monitors, alarms, and notifications for S3 using CloudWatch.
6. Proficiency in Git Bash and GitHub for version control.
7. Technical Environment: AWS DevOps, Informatica, Snowflake, AWS EC2, AWS S3, CloudWatch

Project: Data Analysis and Machine Learning with ZOHO Analytics.

1. Importing, cleaning, and transforming data using SQL.
2. Creating department-wise dashboards.
3. Setting up SNS alerts by department.
4. Developing predictive models for sales.
5. Automating report generation with Jenkins.
6. Data cleaning and feature engineering using Pandas and Matplotlib in Python.
7. Environment: ZOHO Analytics, Jupyter, Jenkins.