DevOps engineer with five years of experience building CI/CD and infrastructure from scratch across Azure and AWS. I work as a consultant embedded across varied client environments — greenfield Kubernetes platforms, CI/CD migrations, stateful workloads with GitOps, and cloud cost optimization. Increasingly focused on data and AI platform tooling.
How I work
- Greenfield, then maintainable. I take projects from zero to running production, then optimize for the long tail — the team picking it up six months later should be able to read the repo and understand what’s going on without me.
- Pragmatic toolchain choices. Most engagements span more than one CI system, IaC tool, or cloud. I pick what fits the client’s existing operations, not what’s trendiest.
- Cost as a first-class concern. Migrations and architecture decisions are evaluated on monthly run-rate, not just technical merits.
- Communication over heroics. I’d rather surface a risk early than rescue a deploy at 2 a.m.
Currently exploring
- Data and AI platform tooling — Dagster for orchestration, RAGFlow for retrieval-augmented workflows, and a custom ClickHouse MCP server for LLM-based access to internal data.
- GitOps for stateful workloads — making CloudNativePG, ClickHouse, and similar systems reproducibly deliverable via Flux and ArgoCD across environments.
- Multi-cloud IaC portability — patterns for keeping Bicep and Terraform code aligned in ways that make porting between Azure and AWS a refactor, not a rewrite.
Engagement model
I work through SlickCloud as an embedded DevOps engineer on long-running client projects, typically owning CI/CD and platform infrastructure end-to-end. Most engagements involve more than one toolchain (current rotation: GitHub Actions, GitLab CI, Azure Pipelines), and most cross either Azure or AWS — sometimes both.