π Iβm currently working on Architecting scalable cloud infrastructure and robust CI/CD pipelines using Azure DevOps, Terraform, and Kubernetes, with increasing focus on AI-assisted automation and cost-optimized platform engineering.
π― Iβm looking to collaborate on Advanced Azure DevOps workflows, infrastructure automation, multi-cloud Kubernetes deployments (AKS & GKE), and AI-agent powered DevOps tooling (Claude, GitHub Copilot, Gemini).
π€ Iβm looking for help with Mastering advanced GitOps patterns, scaling complex Helm chart deployments across multiple environments, and integrating agentic AI workflows (Claude Code, MCP) into platform engineering.
π± Iβm currently learning The finer details of multi-cluster Kubernetes management, Site Reliability Engineering (SRE) best practices, deepening my DevSecOps integrations, Model Context Protocol (MCP) for tool-connected AI, and FinOps cost optimization strategies.
π¬ Ask me about Azure DevOps, Kubernetes, Terraform, Helm, CI/CD automation, AI-assisted development (Claude, GitHub Copilot, Gemini), and my journey transitioning from DBA/DWH roles into full-time DevOps.
β‘ Fun fact I bring 14 years of IT experience to the table, meaning I've navigated the complete evolution from traditional data warehousing all the way to modern, cloud-native DevOps and platform engineering!
- Claude (architecture, design, and documentation assistance)
- Claude Code (agentic coding assistant and CLI for development workflows)
- GitHub Copilot (AI pair programmer for day-to-day coding tasks)
- Google Gemini (for research, prototyping, and code assistance)
- OpenAI / ChatGPT (for automation scripts, infra templates, and knowledge workflows)
- Model Context Protocol (MCP) concepts for connecting tools and external systems to AI
- Agentic patterns: AI-driven refactoring, test generation, runbook creation, and incident analysis
- Using AI to accelerate Terraform module authoring, Helm chart scaffolding, and CI/CD pipeline definitions
- Applying FinOps principles (Inform, Optimize, Operate) to Azure, AWS, and GCP environments
- Cost visibility and allocation across teams, projects, and environments
- Rightsizing, reserved instances, and savings plans with platform guardrails
- Kubernetes cost insights (node sizing, pod requests/limits, autoscaling impact)
- Integrating cost signals into CI/CD, infrastructure as code, and platform engineering workflows
- Partnering with engineering and finance to drive continuous optimization


