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AI Platform & Agentic AI Infrastructure Engineer
Agentic AI · MLOps/LLMOps · Kubernetes & HPC · Research Fellow, University of Bologna
I build autonomous AI that acts on infrastructure — with a human at the gate.
I design and operate the platforms that let AI agents and ML models run in production without anyone losing control of them: approval gates, scoped RBAC, model registries, replay and audit trails. I came to it the unusual way round — seven years as IT and network administrator of a combined-cycle power plant of more than 1,000 MW, where there is no staging environment and a bad change measures in megawatts, then a PhD in high-performance computing.
📍 Bologna, Italy · remote across the EU · 🧭 mskazemi.com · 💼 open to full-time · contract · freelance
ExaMLOps — production MLOps platform for HPC
My role: architect and lead developer.
| Problem | Sixteen partners on a EuroHPC consortium each needed to train, version, govern and serve models on a Tier-0 supercomputer — with no shared platform to do it on. |
| What I built | An end-to-end MLOps platform: a partner registers a model, the platform trains, versions, governs and serves it, behind a sysadmin approval gate. |
| Proof | In production at LuxProvide (MeluXina), for the EuroHPC SEANERGYS project. |
Prefect · MLflow · Ray Serve · Slurm · FastAPI · React
KubeIntellect — a human-governed AI SRE for Kubernetes
My role: creator and first author of the peer-reviewed paper.
| Problem | Diagnosing a live Kubernetes fault means correlating kubectl, Prometheus and Loki by hand under time pressure — and any tool that fixes it automatically is a tool nobody will run in production. |
| What I built | Ask the cluster a question in plain English. It gathers live evidence, works out what is actually wrong, and executes the remediation — pausing for explicit human approval before it changes anything. |
| Proof | Peer-reviewed in the Journal of Grid Computing (2026), 10.1007/s10723-026-09837-6 · live demo · pip install kubeintellect |
That it acts, behind a gate, is the whole point — it is what separates it from a chat window next to a terminal. Domain agents cover logs, metrics, RBAC, lifecycle, scheduling, exec and proxy; the supervisor is a LangGraph state machine with PostgreSQL checkpoints, and a code-generator agent writes and validates new tools at runtime with registration routed through the same human approval.
Python · LangGraph · FastAPI · Kubernetes · PostgreSQL
AOBench — evaluation and permission infrastructure for AI agents
My role: creator.
| Problem | Agent benchmarks score whether the answer looked correct. In operations, an agent that reaches the right answer by exceeding its permissions has failed. |
| What I built | A role-aware, permission-enforced benchmark for LLM agents doing real HPC operations work: a policy violation hard-fails the task, however correct the output. |
| Proof | 88 tasks across 10 categories × 5 roles · archived with a DOI · paper under review. |
Python · MCP · Slurm · RBAC
| Project | What it does | Evidence | Stack |
|---|---|---|---|
| YazSes | Offline voice dictation that never phones home. Hold a key, speak, release — speech-to-text runs on your own CPU and the words are typed into whatever window has focus. Works on Wayland, where most dictation tools silently fail. | Apache-2.0 · cross-platform · built in the open by outside contributors, good first issues tagged · measured accuracy, published method | Python, faster-whisper, Linux/macOS/Windows |
| NovaFabric | A time machine for AI systems — capture, replay, diff and audit any agent or HPC run, with no code changes. Self-hosted. | Apache-2.0 · experimental · novafabric.ai | Python, OpenTelemetry |
| kube-q | CLI and Python SDK for KubeIntellect — pip install kube-q | Streaming responses, Rich TUI · AGPL-3.0 | Python |
| Project | What it does | Evidence |
|---|---|---|
| GRAAFE | Graph neural network that anticipates compute-node anomalies on exascale HPC — trained offline, served online through a Kubeflow pipeline on live telemetry. | Published, FGCS 2024 · CINECA Marconi100 |
| HazardNet | Thermal-hazard prediction for datacenters, over a year of telemetry from 3,312 nodes of CINECA's Marconi A2. Six-hour horizon, chosen with the facility manager. | Published, FGCS 2024 · 1 GB dataset on Zenodo, CC BY 4.0 |
| Area | Tools |
|---|---|
| Platform & infrastructure | Kubernetes · OpenShift · Helm · Terraform · Docker · Linux · Azure |
| Agentic AI & LLM | LangGraph · MCP · A2A · RAG · vLLM · FastAPI |
| MLOps & pipelines | MLflow · Kubeflow · Prefect · Ray Serve · KServe |
| HPC | Slurm · MPI · OpenMP |
| Observability | Prometheus · Grafana · Loki · OpenTelemetry |
| ML systems | Python · PyTorch · GNNs · TCN/LSTM · anomaly detection · time-series telemetry at datacenter scale |
PhD: Design, Analysis, and Management of High-Performance Computing Systems · University of Bologna (2018–2022)
Selected peer-reviewed work:
| Paper | Venue | Year |
|---|---|---|
| KubeIntellect: A Modular LLM-Orchestrated Agent Framework for Kubernetes Management | Journal of Grid Computing | 2026 |
| M100 ExaData: A Data Collection Campaign on CINECA's Marconi100 Tier-0 Supercomputer | Nature Scientific Data | 2023 |
| GRAAFE: GRaph Anomaly Anticipation Framework for Exascale HPC Systems | FGCS | 2024 |
Three open datasets, 26 GB in total: M100 ExaData, the HazardNet thermal dataset (first author) and PM100 — free to download, no registration.
Full publication list and current citation counts → Google Scholar · ORCID · dblp
Reviewer for IEEE TCAD · FGCS · Journal of Grid Computing · SC · ACM CF · DATE · PDP · AsHES. PC member: PDP 2025 · PDP 2026 · AsHES 2026.
Hiring for AI Platform · Agentic AI · MLOps/LLMOps · Kubernetes? Remote across the EU, on CET. Full background and experience on LinkedIn — or email mohsen@mskazemi.com.
Need a consulting engagement or an audit? 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
→ Engagements, rates and how it works
Website · About · LinkedIn · GitLab · Mastodon · Scholar · ORCID
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