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Hi, I'm

Mir Hyder Ali

|

Building the infrastructure that powers tomorrow's AI — from bare metal to Kubernetes, one GPU cluster at a time.

View My Work Get in Touch Resume
engineer.py 
class Engineer:
  name  = "Mir Hyder Ali"
  role  = "HPC Systems Engineer"
  stack = ["GB200", "H100",
           "Kubernetes", "Slurm"]
  based = "Dallas–Fort Worth, TX"
  open  = True

print("Let's build something!")

About Me

Mir Hyder Ali

I'm an HPC Systems Engineer with 3+ years of experience deploying and operating large-scale GPU compute clusters for enterprise and research AI workloads. I specialize in NVIDIA Blackwell (GB200 NVL72) and Hopper (H100) systems, InfiniBand fabric, high-performance storage, and Kubernetes-based AI workloads — with heavy use of Ansible and Jenkins to automate the whole lifecycle.

Based in the Dallas–Fort Worth Metroplex. Currently building bleeding-edge HPC and cloud infrastructure at NorthMark Compute & Cloud (NMC²), whose NVIDIA-powered supercomputing platform accelerates ML, simulation, and scientific research. I hold an M.S. in Computer Science from Lewis University (2025) and am passionate about bridging advanced hardware with intelligent software automation.

3+Years Experience
100+GPU Nodes Deployed
M.S.Computer Science
Email Me LinkedIn

My Education

M.S. Computer Science Lewis University · 2025
2023 – 2025

M.S. Computer Science

Lewis University · Romeoville, IL

Graduate research in AI and software engineering. Co-authored a peer-reviewed paper on GenAI-driven unit test generation published by Springer (ITNG 2025). Also served as IT Graduate Assistant — administering campus network infrastructure.

AI / MLSoftware EngineeringResearchSpringer Publication
2019 – 2022

B.Tech

Lords Institute of Engineering & Technology · Hyderabad, India

Bachelor of Technology — engineering foundations spanning mathematics, electronics, and core systems design.

EngineeringSystemsMathematics

Technical Skills

🖥️

HPC & Compute

  • NVIDIA GB200 / H100
  • Kubernetes & Slurm
  • InfiniBand HDR/NDR
  • NVMe Storage Arrays
☁️

Cloud & DevOps

  • AWS & Azure
  • Docker & Terraform
  • Prometheus / Grafana
  • CI/CD Pipelines
⚙️

Systems & Network

  • Linux (RHEL / Ubuntu)
  • IPMI / iDRAC / BMC
  • Bash & PowerShell
  • NVLink Fabric
🤖

Software & AI

  • Python Automation
  • REST APIs & Git
  • AI/ML Infrastructure
  • Research & Analysis

Work Experience

Jun 2026 – Present

HPC Systems Engineer

NorthMark Compute & Cloud (NMC²) · Dallas–Fort Worth, TX · Hybrid

Building and automating NVIDIA GPU supercomputing, cloud, and multi-petabyte HPC storage for NMC²'s advanced high-performance computing platform.

NVIDIA GPUAnsibleJenkinsSlurm
  • Build, provision, and operate large-scale NVIDIA GPU compute clusters on NMC²'s HPC platform, supporting ML training, simulation, and scientific research workloads.
  • Automate bare-metal provisioning, configuration management, and cluster lifecycle with Ansible and Jenkins CI/CD pipelines for repeatable, hands-off deployments.
  • Support high-performance, scalable storage and low-latency InfiniBand / NVLink fabric to meet demanding HPC throughput and I/O targets.
  • Harden monitoring, alerting, and observability across compute, storage, and network layers to keep production supercomputing infrastructure highly available.
AnsibleJenkinsLinuxKubernetesPython
May 2023 – Jun 2026

AI HPC Infrastructure
Lead Engineer

Wistron Corporation · Irving, TX

Deployed NVIDIA GB200 NVL72 & H100 GPU clusters for Fortune 500 AI clients — full rack lifecycle, InfiniBand fabric, and automated monitoring.

NVIDIA GB200 NVL72 · 4 Models Explore in 3D
NVIDIA GB200InfiniBandKubernetesSlurm
  • Deployed and commissioned NVIDIA GB200 NVL72 and H100 GPU clusters for Fortune 500 AI clients, managing full rack-to-rack lifecycle from bare-metal provisioning to OS imaging.
  • Configured and validated InfiniBand HDR/NDR fabric, NVLink topology, and NVMe-oF storage arrays to meet AI training performance SLAs.
  • Automated cluster health monitoring, GPU utilization dashboards, and failure alerting with Python, Bash, Prometheus, and Grafana.
  • Coordinated with NVIDIA field engineers on firmware, driver, and OFED stack qualification for production AI workloads.
PythonLinuxPrometheusGrafana
Jan – May 2023

IT Graduate Assistant

Lewis University · Romeoville, IL
Windows ServerVMwareActive Directory
  • Administered campus network infrastructure including Windows Server, Active Directory, and VMware virtualisation environments.
  • Provided Tier 2 technical support for faculty and students; resolved hardware, software, and network issues.
NetworkingTier 2 Support

My Projects

✦ scratch to peek
🧊
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NVIDIA GB200 NVL72 — 3D Models

Generated and optimised four production-accurate 3D models of NVIDIA GB200 NVL72 hardware using Tripo3D AI — including the BIANCA compute modules (with and without ConnectX-7), the NVL72 rack chassis, and the full compute tray with cabling. Draco-compressed for web delivery (55 MB → ~6 MB each) and integrated into an interactive Three.js viewer.

Tripo3D AIThree.jsDracoGLTFNVIDIA GB200
✦ scratch to peek
🌐
</>

Portfolio 2026

This site — built from scratch with vanilla HTML, CSS, and JS. Features a WebGL-inspired aurora canvas background, glassmorphism design system, and an interactive 3D hardware viewer showcasing NVIDIA GB200 NVL72 hardware. The BIANCA compute module models were generated using Tripo3D AI and compressed for web with Draco.

HTMLCSSJavaScriptCanvas API
✦ scratch to peek
🔬
</>

Generative AI for Software Testing

Co-authored research paper exploring LLM-based automated test case generation. Evaluated GPT-4 and CodeLlama on real-world codebases, achieving 78% valid test coverage with minimal human input.

PythonOpenAI APILangChainResearch
✦ scratch to peek
📊
</>

HPC Cluster Health Monitor

Real-time dashboard tracking GPU utilization, temperature, InfiniBand link health, and job queues across 100+ nodes. Reduced mean-time-to-detect hardware failures from hours to minutes.

PythonPrometheusGrafanaBash
See All on GitHub

Published Research

Published · Springer ITNG 2025 · pp. 433–444

The Use of GenAI in Graph-Based Unit Testing

Abubakr S. Masood  ·  Mir H. Ali  ·  Mohammed W. Amair  ·  Ziad A. Al-Sharif  ·  Safwan Omari

Source Code Functions & Classes
AST / CFG
Graph Model Call & Control-Flow
Prompt
GenAI / LLM Context-Aware Generation
Output
Unit Tests High Path Coverage

This paper explores using Generative AI to augment graph-based unit test generation. Source code is parsed into call graphs and control-flow graphs, encoding structural relationships between functions and execution paths. These graphs provide rich contextual prompts to a large language model, enabling it to generate unit tests that exercise deeper execution paths — achieving higher structural coverage than conventional auto-generated tests.

2025Published
5Co-Authors
12 ppPages
SpringerPublisher
View Published Paper Masood, A.S., Ali, M.H., et al. The Use of GenAI in Graph-Based Unit Testing. ITNG 2025, Springer.

Beyond The Screen

Gaming & Tech Enthusiast

When I'm not pushing commits or deploying GPU clusters, I'm deep in a gaming session or obsessing over the perfect desk setup. I treat my rig with the same attention to detail as my infrastructure — every RGB strip calibrated, every cable routed.

Currently obsessed with: PC building, FPS games, and chasing that perfect refresh-rate feeling.

⚡ PC Gaming 🖥️ Setup Building 🎮 FPS Games 🔧 Hardware
terminal — hint

$ there's a classified file hidden on this site

$ type sudo anywhere to access it

# login: the name you already know

OPERATOR Mir Hyder Ali _

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Mir Hyder Ali
Mir Hyder Ali HPC Systems Engineer NorthMark Compute & Cloud (NMC²) · Dallas–Fort Worth, TX
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📍 Dallas–Fort Worth Metroplex, Texas  ·  Open to remote & on-site opportunities

secure_vault — classified — [ESC to abort]
$