Hi, I'm
Building the infrastructure that powers tomorrow's AI — from bare metal to Kubernetes, one GPU cluster at a time.
class Engineer: name = "Mir Hyder Ali" role = "AI HPC Infrastructure" stack = ["GB200", "H100", "Kubernetes", "Slurm"] based = "Irving, TX" open = True print("Let's build something!")
I'm an AI HPC Infrastructure Engineer with 3+ years of experience deploying and managing large-scale GPU compute clusters for enterprise AI clients. I specialize in NVIDIA Blackwell (GB200 NVL72) and Hopper (H100) systems, InfiniBand fabric, and Kubernetes-based AI workloads.
Based in Irving, Texas. Currently building bleeding-edge AI infrastructure at Wistron. I hold an M.S. in Computer Science from Lewis University (2025) and am passionate about bridging advanced hardware with intelligent software automation.
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.
Lords Institute of Engineering & Technology · Hyderabad, India
Bachelor of Technology — engineering foundations spanning mathematics, electronics, and core systems design.
Deploying NVIDIA GB200 NVL72 & H100 GPU clusters for Fortune 500 AI clients — full rack lifecycle, InfiniBand fabric, and automated monitoring.
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.
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.
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.
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.
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.
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.
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I'm always open to new connections, opportunities, and conversations. Use the chat widget to send a message directly to my inbox.
📍 Irving, Texas · Open to remote & on-site opportunities