Technical Operations & Systems Portfolio
Technical Operations & Systems Portfolio
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Building, Testing, and Improving Technical Systems
I am a technical systems and quality assurance professional with experience building, configuring, testing, troubleshooting, and improving hardware and software environments.
My work combines hands-on hardware integration with structured system testing, failure analysis, technical documentation, and operational problem-solving. This portfolio presents selected projects involving GPU compute platforms, custom workstations, local AI, power and thermal management, component compatibility, system reliability, and the decisions made when systems did not perform as expected.
How This Connects to Cyber Excavation
Cyber Excavation applies the same systems-oriented approach shown in this portfolio to small-business workflows: understand the system, identify constraints, test real behavior, document the process, and refine the outcome.
I help small businesses turn repetitive, unclear, or inefficient work into practical AI-assisted workflows with human review, documentation, and ongoing refinement.
Multi-GPU Compute Platform
2021-2022
Designed, assembled, configured, tested, and operated a compute platform that scaled to six NVIDIA RTX 3090 GPUs. The system used a single motherboard, PCIe risers, Windows 10, monitoring software, and two 1,300-watt power supplies.
Key result: Operated all six GPUs together while diagnosing thermal throttling, failed PCIe risers, disappearing devices, driver issues, unexpected shutdowns, and power-related failures.
Outcome: The platform required a 200-amp electrical-panel upgrade, dedicated power circuits, room-level cooling improvements, and continued monitoring to remain reliable.
Lesson: Complex systems require active testing, failure analysis, and operational ownership—not simply compatible components.
Business relevance: Demonstrates technical ownership, constraint management, troubleshooting, reliability planning, and operational decision-making.
Local AI & Machine Learning Experimentation
2022
Repurposed the six-GPU platform to evaluate local AI and machine-learning workloads. I removed the original mining configuration, installed GPT4All, enabled CUDA acceleration, and tested local language-model inference across the available RTX 3090 GPUs.
Key result: Evaluated local LLM operation, prompt workflows, GPU utilization, and agent-style experimentation while identifying the limitations of the existing system architecture.
Outcome: The PCIe riser-based design created data-transfer and communication bottlenecks that prevented the GPUs from functioning as one unified memory pool.
Lesson: High aggregate GPU capacity does not guarantee strong AI performance; system architecture must match the communication and memory requirements of the intended workload.
Business relevance: Demonstrates practical AI evaluation, output validation, architecture-fit analysis, and knowing when not to force the wrong solution.
Custom AI Workstations
2022 - Present
Designed, assembled, configured, and tested dedicated workstations for local AI, image generation, software development, data analysis, content creation, and gaming. The systems repurposed RTX 3090 GPUs alongside new hardware, Windows 11, updated NVIDIA drivers, and CUDA-compatible software environments.
Key result: Delivered purpose-built systems tailored to different workloads, user requirements, performance expectations, and budgets.
Outcome: Completed multiple workstation configurations that were retained, sold, or gifted, while maintaining a separate system for continued personal experimentation and technical development.
Lesson: Reliable system design requires balancing performance, compatibility, thermal management, maintainability, cost, and the actual intended workload.
Business relevance: Demonstrates requirements analysis, system design, user support, performance balancing, and maintainable implementation.
What This Portfolio Demonstrates
What This Portfolio Demonstrates
Technical Operations
Building, configuring, maintaining, and improving technical systems.
Quality Assurance
Testing real behavior, reproducing issues, validating fixes, and documenting results.
Systems Thinking
Understanding how hardware, software, power, cooling, people, and workflows interact.
Practical AI Judgment
Evaluating where AI is useful, where it fails, and where human review is required.
About This Portfolio
About This Portfolio
This portfolio documents independent technical projects involving GPU systems, local AI, custom workstations, troubleshooting, and systems operations.
It is intended to show how I think, how I build, how I respond when systems fail, and how I turn technical lessons into clearer, more reliable systems.
Systems Approach
Systems Approach
My work centers on improving technical systems and business workflows through structured testing, documentation, troubleshooting, practical AI use, and clear operating processes.
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