# Mohsen Seyedkazemi Ardebili — AI Platform & Agentic Systems Engineer > AI Platform & Agentic Systems Engineer and independent consultant building agentic AI, MLOps and reproducible execution systems across Kubernetes, OpenShift and HPC. Source: · Author: Mohsen Seyedkazemi Ardebili · This is the Markdown twin of the HTML page; the HTML is canonical. --- AI Platform & Agentic Systems Engineer · Independent Consultant I build autonomous AI that acts on infrastructure. Not a chatbot that explains your cluster — systems that observe it, reason about failures, and execute remediation, with a human at the gate. LLM agents for Kubernetes & HPC, anomaly detection on Tier-0 supercomputers, and the MLOps to run it in production. - BASED Italy · remote across Europe & internationally - EXP 6 yrs 1,000 MW plant → PhD HPC - OSS KubeIntellect · AOBench · YazSes Deep dives [KubeIntellect](https://mskazemi.com/projects/kubeintellect/) [NovaFabric](https://mskazemi.com/projects/novafabric/) [AOBench](https://mskazemi.com/projects/aobench/) [IDKMesh](https://mskazemi.com/idkmesh/) telemetry in → root-cause reasoning → gated action → back to telemetry // profile ## From a power plant's networks to the control plane of a datacenter. I spent nearly nine years in enterprise IT and network operations — six of them as the **IT & Network Administrator** of a 1,000+ MW combined-cycle power plant, running the network across eleven operational zones, where downtime is not an abstraction. Then I did a PhD in High-Performance Computing at the University of Bologna. That path gives me a lens most ML researchers don't have: I care about uptime, observability, and correctness _in production_ — not just benchmark numbers. Today I design autonomous control for infrastructure: systems that detect failures, reason about root cause, and propose or execute remediation behind human-approval gates. I was a Postdoctoral Research Fellow at DEI, University of Bologna from August 2022 to September 2026, working across EU Horizon and EuroHPC projects including DECICE and SEANERGYS. I now work as an independent consultant on AI platform and MLOps systems, alongside open-source systems I build and maintain. // systems — research-grade work, in the open ## AI SRE and AIOps systems for Kubernetes and HPC. Things that act, with the receipts — KubeIntellect works on a live Kubernetes cluster and stops for a human before it changes anything; AOBench measures whether an agent stays inside its permissions, against reproducible HPC snapshots. A modular, LLM-orchestrated multi-agent framework for **end-to-end Kubernetes operations** — root-cause analysis, diagnosis, and human-gated cluster actions across the full API surface (read, write, exec, delete, RBAC, lifecycle). A stateful LangGraph supervisor coordinates domain agents; in the published architecture a Code-Generator agent synthesised and validated new tools at runtime. Published in the _Journal of Grid Computing_ (2026, 24(3):17). - Stateful LangGraph supervisor + PostgreSQL checkpoints - Human-in-the-loop approval on every mutating operation - Runtime tool synthesis with AST validation and a Kubernetes API whitelist _(paper architecture)_ - Deployed on Azure AKS — OpenAI-compatible FastAPI backend ``` $ kq "why is payments-api crashlooping?" → inspecting pods, events, logs… root cause: OOMKilled — memory limit 256Mi exceeded on restart. proposed fix: raise limit to 512Mi approve? [y/N] ▍ ``` Agent Operations Benchmark — a trace-driven, role-aware, RBAC-enforced benchmark for LLM agents that operate HPC systems. It asks the blunt question: _can an autonomous agent be trusted to run a supercomputer?_ — and scores the answer against real operator roles and hard policy constraints. - 88 tasks across 10 question categories × 5 operator roles (release v0.4.1) - 29 deterministic environment bundles — 23 synthetic, 6 built from real Marconi100 telemetry - Permission-enforced: a policy violation hard-fails the task, whatever the answer said - Scored on 7 weighted dimensions, rolled into a CLEAR scorecard (Cost · Latency · Efficacy · Assurance · Reliability) - 16 model systems evaluated on the 59-task dev split, every headline number tied to a frozen run ID Verified swarm engineering — an open research laboratory asking whether humans and AI agents can turn uncertain goals into trustworthy software by separating _generation_ from _acceptance_. Its first installable tool, `idkmesh gate-audit`, measures what a review panel is actually worth: effective independent votes rather than head-count, error-correlation structure, and the breach rate of seeded known-bad candidates. - E017 measured 25 independently seeded test oracles — programs, over a 72-candidate corpus with ground truth from executing hidden tests — at an effective panel size of 1.00 - The standard `N/(1+(N−1)ρ)` heuristic predicted 1.66, so it overstates exactly where it is relied on - Changing the aggregation rule cut error 3.7×; growing the panel bought nothing - E016 is retained as a **negative result**: 20 live LLM verifiers did not verify, so no AI review panel has been measured here - Negative results and failed experiments are kept, not hidden — evidence must survive independent scrutiny Graph anomaly-anticipation for exascale HPC — topology-aware node-failure prediction running in production on CINECA's Tier-0 Marconi100, published in _FGCS_. - Models the machine as a graph and predicts, per node, the probability of an anomaly in a future window - Benchmarked by AUC against per-node DNN, gradient-boosting, random-forest and decision-tree baselines - Trained offline, then served online as a Kubeflow pipeline reading live ExaMon telemetry over MQTT - The paper reports negligible added overhead versus running the monitoring system alone Thermal-hazard prediction for datacenters — multi-modal deep learning forecasting thermal failures fast enough to act, with explanations operators trust. - A year of inlet/outlet temperature and power telemetry from 3,312 nodes of CINECA's Marconi A2 - Temporal Convolutional Network, LSTM and SVM models over a rolling time window - Six-hour prediction horizon — chosen with the facility manager, not with a grid search - Training data published openly on Zenodo so the result can be reproduced An end-to-end MLOps platform for HPC workload management, built for the EuroHPC **SEANERGYS** project and now running in production on European HPC infrastructure. Architect, lead designer and main developer; WP3 task lead at Bologna. It is **model-agnostic and multi-tenant** — any of the sixteen consortium partners registers a model and the platform auto-discovers and operationalises it (train → version → govern → serve → monitor) without ever owning the model code. The whole loop, on a real supercomputer, behind an operator approval gate. - Auto-discovery training pipelines in Prefect: every registered model × dataset pair runs train → evaluate → log → promote - Slurm adapter for HPC job orchestration; a DataPlane bridge carries messages off the machine - MLflow registry with a multi-stage lifecycle and a YAML model registry with per-environment overlays - Ray Serve multi-model serving with batched inference; MinIO for artifacts - A sysadmin approval gate stands between a promoted model and the serving fleet — nothing goes live unattended - Prometheus, Grafana and Loki for metrics and logs; React 19 + FastAPI dashboard; `exa` operator CLI The operator's companion for KubeIntellect — a CLI and Python SDK (`kq`) exposing the full agent API with streaming output and a human-approval UX, built for CI/CD. - Full KubeIntellect API coverage from the terminal - Streaming Rich TUI · pipeline-friendly output // ships — open-source systems ## Research is half of it. I also ship. Privacy-first, local-first tools — built to be installed and used, not just cited. **NovaFabric** is open-source, self-hosted replay and evidence infrastructure for **AI agents and agentic systems**. It captures agent executions as **portable Run Capsules** for replay, behavioral/structural diff, lineage, cryptographic provenance, assurance and audit — with no application-code changes. It also supports local, Docker, Kubernetes and SLURM/HPC execution environments. - Zero-code-change capture · four explicit replay modes (exact / mocked / semantic / forensic) - Cryptographic seal: DSSE signature + RFC 3161 timestamp + append-only Merkle log - Structured run-to-run diff as a CI regression gate; signed evidence bundles verify without it installed - Runs offline from a laptop to an HPC cluster — no cloud, no account - Apache-2.0 · beta / pre-v1 · core local workflows are self-hosted and offline-first Hold a key, speak, release — fully **on-device** voice dictation that types into any app and runs voice commands. No cloud, no subscription, no data leaving your machine. Shipped and maintained across multiple releases. - On-device faster-whisper (CPU int8) — no GPU, no network, no account - Also transcribes recordings and captures whole meetings with speaker labels - Editor/terminal voice commands; Neovim LSP context; accessibility-first design - Linux · macOS · Windows — APT, Snap, pipx/PyPI - 23 releases on PyPI · 401 commits · 10 contributors A meta-framework for taking a vague idea to production without losing context, evidence, or decision rationale — for humans and AI agents alike. - VisionForge → SOTAForge → DesignForge → BuildForge - Deterministic verification gates: schema, evidence, traceability — no LLM in the gate - `v2p` CLI + FastAPI/HTMX governance portal // research ## Peer-reviewed research in HPC, MLOps and AI operations. - Journal of Grid Computing · 2026 · first author [KubeIntellect: A Modular LLM-Orchestrated Agent Framework for End-to-End Kubernetes Management](https://doi.org/10.1007/s10723-026-09837-6) - Scientific Data (Nature Portfolio) · 2023 [M100 ExaData: A Data Collection Campaign on CINECA's Marconi100 Tier-0 Supercomputer](https://doi.org/10.1038/s41597-023-02174-3) - SC'23 Workshops (ACM) · 2023 [PM100: A Job Power Consumption Dataset of a Large-Scale Production HPC System](https://doi.org/10.1145/3624062.3624263) - Future Generation Computer Systems · 2024 [GRAAFE: GRaph Anomaly Anticipation Framework for Exascale HPC Systems](https://doi.org/10.1016/j.future.2024.06.032) - Future Generation Computer Systems · 2026 · first author [Elevating Datacenter Resilience with ThermADNet: A Thermal Anomaly Detection System](https://doi.org/10.1016/j.future.2025.108311) - DATE · 2021 · first author [Prediction of Thermal Hazards in a Real Datacenter Room Using Temporal Convolutional Networks](https://doi.org/10.23919/date51398.2021.9474116) [All 17 publications, with DOIs and BibTeX →](https://mskazemi.com/publications/) ### Program committee PDP 2025 · PDP 2026 · AsHES 2026 ### Reviewer IEEE TCAD · FGCS · J. Grid Computing · SC · ACM CF · DATE · PDP · AsHES ### Supervision 2 PhD co-advisees · 5 MSc theses · Lab of Big Data Architectures, UniBo // stack ## The toolchain behind the work. ### Agentic AI / ML ### MLOps & serving ### Cloud-native & infra ### Observability ### HPC ### Foundations // hire me ## Hire a freelance AI platform, agentic AI, MLOps and Kubernetes engineer. Fixed-price starter audits to begin low-risk, or project work at a senior day rate. Based in Italy, I work remotely across Europe and internationally (Europe/Rome time zone). I build systems your team can maintain after I'm gone — not black boxes. ### Kubernetes reliability & AIOps Health-checks, observability, hardening, incident root-cause, and ops automation with human-approval gates. ### MLOps & ML in production Get models out of the notebook: registry, serving, drift detection, governed retraining, monitoring — the stack I run in production on EuroHPC. ### Production LLM agents Agents that act on real systems — tool-use, human-in-the-loop safety, tracing, and an audit trail you can defend. Full details, how an engagement runs, and the questions people usually ask → [**mskazemi.com/hire**](https://mskazemi.com/hire/). Free 30-minute scoping call → [mohsen.seyedkazemi@gmail.com](mailto:mohsen.seyedkazemi@gmail.com?subject=Freelance%20enquiry). // questions ## Questions people ask. ### Who is Mohsen Seyedkazemi Ardebili? Mohsen Seyedkazemi Ardebili is an AI Platform & Agentic Systems Engineer and independent consultant. He builds AI platform, MLOps and agentic systems across Kubernetes, OpenShift and HPC, including KubeIntellect for human-governed Kubernetes operations and NovaFabric for replayable, verifiable execution evidence for AI agents. ### What is an AI SRE, and how is it different from a chatbot for Kubernetes? An AI SRE investigates and **acts** on a live cluster, where a chatbot only explains. It gathers its own evidence from real tools — kubectl, Prometheus, Loki — reasons about what is actually failing, and then executes the fix. The difference that matters in production is the approval gate: a human authorises the change, and the whole chain is left as an audit trail. ### Is Mohsen available for full-time, contract or freelance work? Yes — open to relevant full-time roles, contracts and freelance projects; based in Italy and working remotely across Europe and internationally. Engagements run in three areas: Kubernetes reliability and AIOps, MLOps and machine learning in production, and production LLM agents. Each one can start with a fixed-price audit, so you can see the work before committing to a project. Details are on the [hire page](https://mskazemi.com/hire/). ### What kind of infrastructure problems does he work on? Kubernetes incident root-cause analysis, observability and hardening; model registries, serving, drift detection and governed retraining; and the safety layer around LLM agents — tool boundaries, scoped RBAC, human-in-the-loop gates, tracing and audit trails. The research half is anomaly detection and prediction on Tier-0 HPC clusters. ### What is the background behind the research? Nearly nine years in enterprise IT and network operations, six of them as the IT and network administrator of a combined-cycle power plant of more than 1,000 MW — an environment with no staging and no undo — followed by a PhD in high-performance computing at the University of Bologna. Operations first, research second, which is why the systems ask before they act. // contact ## Let's build infrastructure that runs itself. Open to research collaborations, open-source work, and industry partnerships in AI infrastructure, HPC, and autonomous operations.