Background / Method
David
Vales
Infrastructure taught the method. Software widened the reach. The common thread is tracing the real constraint before choosing the tool.
Enterprise systems came first.
I have spent more than two decades working across enterprise technology, from hands-on systems, networking, storage, and disaster recovery through infrastructure leadership, cloud engineering, data pipelines, automation, governance, and solution design.
That background matters because the problems worth solving rarely arrive as isolated software tickets. They show up as systems problems with technical, operational, and organizational edges that all need to be understood together.
AI is useful when it answers to the system.
My computer science studies and independent development work have increasingly focused on AI and ML, cloud architecture, and the infrastructure required to make modern AI useful in practice.
Professionally, that includes applied AI solutions, backend pipeline ecosystems, RAG and LLM integrations, MCP service architecture, governance, and controls for enterprise data exposed to AI systems. I do not treat AI as a biography. It is another engineering instrument, valuable when it solves the real problem.
Investigate. Prototype. Engineer.
Find the actual limit.
Trace the constraint to its real source, whether it is technical, architectural, operational, economic, or simply inherited assumption.
Replace guesses with evidence.
Build enough of the path forward to learn what is true before committing the full system to it.
Make the useful path durable.
Turn what works into something observable, maintainable, and appropriate for the environment it has to live in.
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