Sireesha Jangam

DevOps Engineer

India3 yrs 6 mos experience

Most Likely To Switch

Key Highlights

Expert in automating CI/CD workflows.
Proficient in managing scalable cloud architectures.
Strong foundation in version control and collaboration.

Stackforce AI infers this person is a DevOps Engineer specializing in cloud infrastructure and CI/CD automation.

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Skills

Core Skills

Ci/cdKubernetesCloud MigrationAws

Other Skills

AWS CloudWatchAWS Code BuildAWS Code DeployAWS Code PipelineAnsibleDockerEKSGitHub ActionsGitLab CI/CDGitlab Ci/CdGrafanaGraphanaJenkinsLinuxPrometheus

About

Hello! I'm a result-oriented DevOps Engineer with a passion for designing, implementing, and managing scalable cloud architectures. My toolkit includes: * Possessing a strong foundation in Git and GitHub for version control and collaboration, coupled with advanced skills in Python for automation and scripting. * Skilled in containerization using Docker, orchestration with Kubernetes, and infrastructure provisioning using Terraform and Ansible. * Proficient in building CI/CD pipelines using Jenkins and GitHub Actions to automate software delivery processes, ensuring rapid and reliable deployment of applications. * Extensive hands-on experience in AWS services, including but not limited to EC2, S3, VPC, RDS, and Cloud Formation. * Proficient in optimizing cloud environments for performance, cost, and security. * Experience with Argo CD for continuous delivery and GitOps practices. * Proficiency in SonarQube for code quality analysis and monitoring. * Familiarity with Prometheus for monitoring and alerting in production environments. * Adept at leveraging DevOps principles to streamline operations and foster efficient, automated workflows.

Experience

Ogoul technology

Junior DevOps Engineer

Apr 2024 – Present · 1 yr 11 mos · Doha, Qatar · Remote

As a Junior DevOps Engineer at Ogoul Technology, I work on automating infrastructure and streamlining CI/CD workflows to support scalable, secure deployments across cloud and on-premise environments.
.Dockerized Laravel and Node.js applications and implemented GitLab CI/CD pipelines for automated builds and deployments.
.Managed Kubernetes clusters (K3s) on VMware, configured autoscaling and load balancing with MetalLB and Ingress Controller.
.Deployed applications to Kubernetes production environments, monitored clusters using Prometheus and Grafana, and configured alerting for proactive issue resolution.
.Used Rancher for Kubernetes cluster administration and managed VM provisioning and load balancing in VMware.
.Migrated infrastructure from AWS to on-prem VMware with minimal downtime, ensuring secure and efficient transitions.
.Contributed to real-time platforms like KalamTime and Werfie, enabling multilingual communication and real-time messaging through robust DevOps practices.

DockerGitLab CI/CDKubernetesPrometheusGrafanaRancher+2

Auropro soft systems private limited

DevOps Engineer

Jul 2022 – Feb 2024 · 1 yr 7 mos

Provisioned and maintained AWS Cloud Infrastructure using Terraform for multiple applications, resulting in a 25% reduction in deployment time.
Deployed applications with AWS Code Build, Code Pipeline, and Code Deploy, achieving a 30% improvement in delivery speed.
Built Docker images for applications in Java, Python, and Django, enhancing deployment consistency by 15%.
Automated end-to-end CI/CD pipelines through Jenkins and GitHub Actions, reducing time-to-market by 40%. This involved configuring build and release pipelines, automating testing, and monitoring pipeline health.
Utilized AWS services (Elastic Beanstalk, EKS, ECR, S3) for flexible and reliable application hosting, contributing to a 20% increase in system reliability.
Orchestrated diverse application deployments, including Python-based applications and microservices, within an Amazon EKS Kubernetes cluster. Implemented CI/CD pipelines, improving scalability by 30% and reducing deployment errors by 15%.
Hosted code repositories on GitHub, applying effective Git branching strategies, leading to a 25% improvement in collaboration efficiency and code integration.
Utilized AWS CloudWatch for comprehensive system monitoring, reducing incident response time by 20% through early detection of issues.
Configured notifications for alarm actions using AWS SNS and Lambda, ensuring a 25% faster response to critical events.
Deployed Python applications, integrating with an RDS database, resulting in a 20% improvement in data retrieval speed and reduced latency in data transactions.