AI and data platform infrastructure
Building robust platform foundations for AI exploration and model development on GCP, including automation, governance, monitoring, and scalable compute paths.
Stockholm, Sweden / GCP / AWS / Azure
Multi-cloud platform engineer, open-source contributor, and practical DevOps writer.
I build cloud foundations, write down the patterns, and keep improving the systems that help teams ship faster without losing reliability, observability, security, or cost control.
What makes me useful
I specialize in designing, provisioning, and improving infrastructure for teams that depend on cloud-native systems. My work sits at the intersection of DevOps, MLOps, observability, IAM, FinOps, and automation.
The value I bring is practical: convert messy requirements into reliable platforms, make deployments repeatable, reduce operational noise, and create systems that engineers trust.
I also document what I learn. The best platform engineers leave behind reusable paths, not private knowledge trapped in one person's head.
History
Building robust platform foundations for AI exploration and model development on GCP, including automation, governance, monitoring, and scalable compute paths.
Working across Terraform, Kubernetes, CI/CD, observability, and cost-aware architecture to support production teams on AWS, GCP, and Azure.
Master of Science in Computer Science from Blekinge Institute of Technology with public work spanning machine learning, web systems, automation, and applied programming.
Selected projects
An MCP server for inspecting Azure infrastructure, AI Foundry, and Entra ID through AI agents.
Open repositoryAn MCP server for querying Grafana dashboards, alerts, and data sources through AI agents.
Open repositoryAn MCP server for debugging, analyzing, and diagnosing Kubernetes clusters directly from AI agents.
Open repositoryAn MCP server for Arize Phoenix covering LLM tracing, evaluation, and dataset management.
Open repositoryOpen source and public work
Work and learning history across infrastructure, DevOps, Python automation, web applications, data science, and cloud engineering.
A long-running public application project that continues to attract attention and acts as a visible early engineering foundation.
Public guides, terminology, cheatsheets, and practice material that turn hands-on work into reusable learning assets.
Recent infrastructure work highlights the current direction: Terraform, analytics platforms, cloud foundations, and production readiness.
Writing
Reliability, delivery speed, IAM, observability, and cost need one shared operating model instead of disconnected tooling.
Read articleSmall modules, clear ownership, state discipline, and reviewable plans make infrastructure automation safer.
Read articleDashboards are not enough. Useful observability starts with signals, ownership, and response paths.
Read articleAI teams need governed environments, scalable compute, artifact paths, cost controls, and simple deployment patterns.
Read articleToolbox
Contact
Open to platform engineering, cloud infrastructure, DevOps, SRE, and AI infrastructure conversations.