Kondal R

DevOps Engineer

Hyderabad, Telangana, India2 yrs 11 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Over 2 years of experience in Cloud and DevOps engineering.
Expert in CI/CD pipelines and Infrastructure as Code.
Proven track record in multi-cloud management and automation.

Stackforce AI infers this person is a Cloud and DevOps Engineer specializing in Infrastructure Automation and CI/CD in the Cloud Computing industry.

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Skills

Core Skills

Cloud Infrastructure ManagementCi/cd PipelinesInfrastructure As CodeContainer OrchestrationSystem ObservabilityCloud Security

Other Skills

AWSAWS CodeDeployAWS CodePipelineAWS IAMAmazon EKSAnalytical SkillsAnsibleAutomationAzureAzure DevOpsBusiness InsightsBusiness Intelligence (BI)Cloud-Native ApplicationsCloudWatchComputer Science

About

I'm a Cloud and DevOps Engineer with over 2 years of experience designing and automating scalable infrastructure using AWS, Azure, and DigitalOcean. I specialize in CI/CD pipelines, container orchestration (Docker, Kubernetes), Infrastructure as Code (Terraform, Ansible), and proactive monitoring solutions. I thrive in collaborative, agile environments and am passionate about building reliable systems that deliver business value.

Experience

2 yrs 11 mos

Total Experience

1 yr 5 mos

Average Tenure

1 yr 7 mos

Current Experience

Interakt techsol

Cloud DevOps Engineer

Nov 2024 – Present · 1 yr 7 mos · Hyderabad, Telangana, India · On-site

1. Managed multi-cloud environments across AWS (EC2, VPC, S3, RDS, Lambda, Route53, CloudWatch), Azure (VMs, App Services, DevOps, Monitor), and DigitalOcean (Droplets, Snapshots, Firewalls), ensuring 99.9% uptime.
2. Automated infrastructure provisioning using Terraform, Ansible, and CloudFormation, resulting in 70% faster deployments and greater consistency.
3. Optimized Linux server administration and automated daily operational tasks with Python and Shell scripting, cutting manual effort by 60%.
4. Designed and implemented CI/CD pipelines with Jenkins, GitHub Actions, Azure DevOps, and AWS DevOps, reducing release cycles by up to 50%.
5. Containerized applications with Docker and orchestrated deployments using Kubernetes, enabling scalable and reliable multi-environment delivery.
6. Improved system observability by setting up monitoring and logging with Prometheus, Grafana, ELK Stack, and CloudWatch, reducing MTTR (Mean Time to Resolution) by 40%.
7. Enhanced collaboration and DevOps workflows through Git, JIRA, and Slack integrations, improving issue resolution speed by 30%.
8. Secured cloud infrastructure by managing IAM, DNS configurations, SSL, and applying compliance best practices, ensuring audit readiness.
9. Deployed AI workloads on RunPod GPU servers, improving workload orchestration and performance for compute-intensive applications.