AI Infrastructure · AI SRE & AIOps · MLOps · HPC
Research Fellow, University of Bologna · Bologna, Italy

I build autonomous AI systems that act on infrastructure — not just explain it.

I came to this the unusual way round. Before the PhD I spent seven years as the IT and network administrator of a combined-cycle power plant of more than 1,000 MW: no staging environment, and a bad change measured in megawatts rather than in error budgets. Then a doctorate in high-performance computing at the University of Bologna, and since then research and platform engineering on EuroHPC-funded projects.

That background is why my systems ask before they act. Everything below is one attempt at the same question: how much of an operator's judgement can a machine take over, and how do you prove afterwards that it was right to?

📍 Bologna, Italy · remote across the EU · 🧭 mskazemi.com · 💼 available for freelance work

Ask your Kubernetes cluster a question in plain English. It gathers live evidence from tools such as kubectl, Prometheus and Loki, works out what is actually wrong — and pauses for your explicit approval before it changes anything.

• It executes cluster operations, behind a human approval gate — that is the whole point, and it is what separates it from a chat window next to a terminal
• LangGraph FSM supervisor with PostgreSQL checkpoints and human-in-the-loop approval gates
• Dynamic Code-Generator agent: writes and validates new tools at runtime, with registration routed through human approval
• Modular domain agents: logs, metrics, RBAC, lifecycle, scheduling, exec, proxy
• Peer-reviewed — Journal of Grid Computing (2026), 10.1007/s10723-026-09837-6

Hold a key, speak, release. Speech-to-text runs entirely on your own CPU and the words are typed into whatever window has focus — editor, browser, terminal, a remote SSH session. No cloud, no account, no subscription.

• Works on Wayland, where most dictation tools silently fail — injection happens at the kernel uinput layer, below the compositor, so GNOME, KDE, sway and Hyprland all behave the same
• Accuracy measured, not asserted: 2.59% WER on LibriSpeech test-clean (small.en), 4.07% with the default base.en — method and repro commands published
• Also transcribes recordings and captures whole meetings with speaker labels, same install
• Built in the open by outside contributors — good first issues are tagged and waiting

Remote across the EU, in three areas — each starting with a fixed-price audit so you can see the work before committing to a project:

• Kubernetes reliability & AIOps — health-checks, observability, hardening, incident root-cause analysis
• MLOps & ML in production — registry, serving, drift detection, governed retraining, monitoring
• Production LLM agents — tool boundaries, scoped RBAC, human-in-the-loop safety, tracing, audit trails

Based in Bologna, Italy — working remotely across the EU on CET.

→ Engagements, rates and how it works

PhD: Design, Analysis, and Management of High-Performance Computing Systems · University of Bologna (2018–2022)

EU projects: SEANERGYS (EuroHPC-JU — MLOps platform architect & lead developer, WP task lead) · DECICE · Graph-Massivizer · EUROPEAN PILOT · REGALE · EPI SGA1

Impact(Google Scholar, read 2026-08-10 — see the live profile for current figures): 218 citations · h-index 8 · i10-index 7, across 17 published or accepted peer-reviewed works. The trend is the interesting part: 9 citations in 2022, 15 in 2023, 24 in 2024, 76 in 2025, and 65 in the first seven months of 2026 — more in the last twenty months than in the preceding eighteen years combined.

Open data. I co-author three CC BY 4.0 datasets from CINECA's Tier-0 supercomputers — roughly 26 GB in total: M100 ExaData (24.8 GB of Marconi100 telemetry), the HazardNet thermal dataset (3,312 nodes, first author) and PM100 (per-job power). Free to download, commercial use permitted.

Full list, with current citation counts → Google Scholar · ORCID · dblp · OpenAlex

PC Member: PDP 2025 · PDP 2026 · AsHES 2026

Reviewer: IEEE TCAD · FGCS · Journal of Grid Computing · SC · ACM CF · DATE · PDP · AsHES

Supervision: 2 PhD co-advisees (ongoing) · 5 MSc theses completed · Lab of Big Data Architectures, UniBo (2020–2024)

Hiring, or need an audit? → mskazemi.com/hire
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