Hovhannes K.

👋 Here is your DevOps Engineer with 5+ years of experience building and maintaining scalable cloud infrastructure and automation systems. I focus on improving deployment speed, system reliability, and making cloud environments easier to manage for development teams working in production. Alongside traditional DevOps, I also work with AI-assisted engineering workflows using Claude, Cursor, OpenAI, and OpenClaw-based automation experiments to improve infrastructure efficiency, debugging speed, and deployment intelligence. My goal is to combine DevOps stability with modern AI-driven automation layers. 👉 Cloud Platforms & Infrastructure I have hands-on experience working across AWS, Azure, and GCP, designing cloud-native architectures that support scalable and high-availability systems. Most of my infrastructure work revolves around AWS environments, where I also experiment with OpenAI and OpenClaw-based automation patterns to improve operational workflows and monitoring. Cloud infrastructure design, optimization, and maintenance remain a core part of my DevOps engineering practice. 👉 Containers & Orchestration I work with Docker to containerize applications and ensure consistent environments across development and production. I use Kubernetes to manage containerized workloads at scale, including AWS EKS, Azure AKS, and Google GKE. This combination of Docker, Kubernetes, and AI-assisted tooling like OpenAI and OpenClaw helps me build systems that are stable, scalable, and easier to operate. I also use Cursor and Claude to speed up troubleshooting and configuration workflows. 👉 CI/CD & Automation I design and manage CI/CD pipelines using Azure DevOps, AWS DevOps toolchains, GitHub Actions, and GitLab CI. My focus is on fully automating the delivery process so deployments are fast, repeatable, and safe. I also integrate AI-assisted improvements using OpenAI and OpenClaw concepts to reduce manual intervention and improve release consistency. Claude and Cursor are part of my workflow for accelerating pipeline debugging and scripting. 👉 DevOps Engineering Approach My approach to DevOps is centered around automation, scalability, and reliability. I combine Docker, Kubernetes, AWS, Azure, and GCP services to build infrastructure that supports continuous delivery and stable production environments. I also experiment with AI-driven workflows using OpenAI, Claude, Cursor, and OpenClaw to make infrastructure more adaptive and intelligent over time. 👉 Languages & System Thinking I use programming and scripting as part of my DevOps workflow to build automation, improve reliability, and connect systems together. Node.js -I use Node.js to build automation tools, backend integrations, and CI/CD support systems, sometimes enhanced with OpenAI and OpenClaw-based automation logic. Python - I use Python for infrastructure automation, deployment scripts, and AI-assisted DevOps workflows, including OpenAI and OpenClaw integrations for smarter automation. Bash/Shell - I regularly use Bash for Linux automation, server management, and CI/CD scripting, often improving workflows using AI-assisted approaches. YAML - I use YAML for CI/CD pipelines, Kubernetes manifests, and infrastructure configuration, sometimes validating structures using AI tools like OpenAI and OpenClaw. SQL - I use SQL for log analysis, troubleshooting, and system validation across production environments.