*a7f2e89(HEAD → main)feat: DevOps Engineer @ ResourceDekho IT Services
DevOps Engineer·ResourceDekho IT Services, Remote·Dec 2025 – Present
Automated infrastructure provisioning and application deployments using Terraform, Ansible, and GitOps practices, ensuring secure, consistent, and compliant environments
Designed and deployed containerized microservices on Amazon ECS, boosting service isolation, scalability, and deployment flexibility across environments
Configured and sustained VPN solutions to enable secure remote access to cloud-hosted infrastructure and applications, delivering encrypted communication and controlled access
Led the migration of manually operated infrastructure to Terraform-based Infrastructure as Code, curbing configuration drift and reinforcing deployment consistency
Enforced GitOps-driven Kubernetes deployments using Argo CD's App-of-Apps architecture, centralizing management and streamlining application synchronization across environments
Administered and optimized Amazon EKS clusters, performing cluster upgrades, capacity planning, and security hardening to safeguard high availability and reliability
Oversaw compliance monitoring and standardized security controls using Sprinto, AWS GuardDuty, and WAF, strengthening threat detection, audit readiness, and compliance tracking
Established monitoring and alerting frameworks using Prometheus, Grafana, Loki, and Fluent Bit, advancing observability, performance tracking, and incident response
Architected and maintained Jenkins CI/CD pipelines to streamline application builds, testing, security scanning, and deployments across Kubernetes environments
Architected and managed Kubernetes cluster for FinTech application infrastructure, ensuring 99.9% uptime
Optimized cloud costs by implementing auto-scaling policies and resource tagging strategies
Established disaster recovery procedures and backup automation for critical FinTech workloads
Enabled VPC peering and cross-account networking for secure connectivity between isolated environments, lowering latency and simplifying inter-service communication
Collaborated closely with development teams to simplify release workflows, sharpen delivery processes, and guarantee reliable software rollouts
Monitored infra with Grafana, Prometheus, and AWS CloudWatch dashboards & alerts
Designed and implemented a secure, scalable multi-account AWS architecture for an organization operating across Development, UAT and Production environments — replacing a flat, single-account structure with a governed, isolated setup using AWS Organizations.
## Architecture
AWS Organizations (Root)
|
IAM Identity Center + Entra ID (SSO)
|
+----------------+----------------+
| | |
Dev OU UAT OU Prod OU
EC2 RDS S3 EC2 RDS S3 EC2 RDS S3
## Key Deliverables
Structured AWS Organizations with dedicated OUs for Dev, UAT and Prod, establishing clear account-level isolation and improving cost visibility by 40%.
Migrated core workloads — RDS, S3, EC2 — from the root account into their respective OUs with zero downtime, reducing operational risk by 60%.
Integrated Microsoft Entra ID with AWS IAM Identity Center for federated SSO across all accounts, cutting manual access provisioning effort by 80% and enforcing consistent RBAC.
Established Service Control Policies (SCPs) and account baselines to maintain compliance and prevent configuration drift across environments.
## Outcome
A production-grade, enterprise-ready AWS landing zone with strong security posture, simplified access management and clear cost/resource accountability across environments — a foundation that scales as the organization adds more accounts or teams.
## Skills & Tools
AWS OrganizationsIAM Identity CenterMicrosoft Entra IDIAMRBACEC2RDSS3Service Control PoliciesCloud MigrationCloud Security
Case study: ShopfloorGPT Deployment using Kubernetes, Terraform & GitLab
$ cat ~/projects/shopfloorgpt.md
ShopfloorGPT Deployment using Kubernetes, Terraform & GitLab
DevOps & Cloud Engineering Case Study
70%Faster infra setup time
80%Less manual release effort
30%Lower AKS compute cost
## Overview
Deployed ShopfloorGPT on Azure Kubernetes Service (AKS) using Terraform for infrastructure provisioning and GitLab CI/CD for automated builds and releases — turning a manual, ad-hoc deployment process into a fully automated, reproducible pipeline.
Deployed ShopfloorGPT on AKS using Terraform, making infrastructure fully reproducible and reducing environment setup time by 70%.
Implemented GitLab CI/CD pipelines for automated builds and deployments, cutting manual release effort by 80% and enabling consistent, repeatable releases.
Optimized and maintained the AKS cluster on an ongoing basis, improving workload performance while lowering compute costs by 30%.
Standardized infrastructure and release workflows into version-controlled Terraform and GitLab pipeline definitions, removing manual, error-prone deployment steps.
## Outcome
A fully automated, cost-efficient deployment pipeline for ShopfloorGPT — from infrastructure provisioning through to production release — with reproducible environments, faster releases and a leaner, better-optimized AKS footprint.
## Skills & Tools
Kubernetes (AKS)TerraformGitLab CI/CDAzureInfrastructure as CodeCloud Cost OptimizationCluster ManagementDevOps Automation
Case study: Kubeadm to Kubespray Migration
$ cat ~/projects/kubespray.md
Kubeadm to Kubespray Migration
DevOps & Cloud Engineering Case Study
70%Less configuration effort
100%Automated, repeatable deploys
MultiCluster-ready modular roles
## Overview
Migrated an existing kubeadm-based Kubernetes cluster to Kubespray, improving cluster reliability, scalability and lifecycle management. Manual shell-script workflows were replaced with Ansible-based automation, standardizing how clusters are provisioned and maintained.
## Architecture
Kubespray Control Node
|
Ansible Playbooks + Modular Roles
|
+----------------+----------------+
| | |
Cluster A Cluster B Cluster C
kubeadm kubeadm kubeadm
-> migrated -> migrated -> migrated
## Key Deliverables
Migrated the existing kubeadm-based Kubernetes cluster to Kubespray, improving cluster reliability, scalability and long-term lifecycle management.
Replaced manual shell-script workflows with Ansible-based automation, reducing configuration effort by 70% and minimizing human error during provisioning and upgrades.
Modularized the entire deployment codebase into reusable Ansible roles, making cluster provisioning consistent and easier to manage across multiple clusters.
Standardized cluster lifecycle operations (scaling, node add/remove, upgrades) through version-controlled, repeatable playbooks instead of ad-hoc scripts.
## Outcome
A standardized, version-controlled Kubernetes deployment pipeline that replaces fragile, manual cluster setup with reusable Ansible automation — reducing configuration effort, minimizing human error and making multi-cluster management consistent and repeatable.
## Skills & Tools
KubernetesKubesprayAnsiblekubeadmInfrastructure as CodeConfiguration ManagementLinuxCluster Lifecycle ManagementShell ScriptingCI/CD