From ambiguous workflow to shipped system.
Clarify the user and operator need, shape a practical specification, design the service boundary, and take the result through validation and operational handoff.
SOFTWARE PRODUCT BUILDER / ENTERPRISE PLATFORM
Build the platform.
Prove the workflow.
AI-native enterprise engineering across APIs, integrations, cloud services, and agent-assisted delivery - from ambiguous need to production-ready system.
POSITIONING Enterprise platform builder with backend depth and a working AI tool layer.
ROLE ALIGNMENTU / 01
AI-NATIVE
PRODUCT BUILDER
PRODUCT BUILDER
01Enterprise systems↗
02APIs + integrations↗
03Cloud + operations↗
04Codex / MCP workflow↗
20+years engineering
398APIs verified
235work records traced
2AI competition wins
01 / ROLE FIT
What I bring to
Enterprise Platform.
Ubiquiti's builder profile sits at the intersection of platform architecture, product judgment, cloud delivery, and fast AI-assisted execution. That intersection is where my recent work is strongest.
APIs, integrations, and production reality.
20+ years across C#/.NET, REST APIs, SQL-backed systems, enterprise integrations, IIS, Azure DevOps, Linux/SSH, and live business services.
Agents that leave evidence behind.
MCP, repository context, structured outputs, Codex-assisted implementation, testing, review gates, and human approval boundaries.
02 / BUILD EVIDENCE
Useful systems,
traceable outcomes.
Selected work showing the shape of my engineering practice: a clear boundary, an implementable workflow, and evidence that survives handoff.
A1MCP / ENGINEERING WORKFLOW
Copilot Agent Skill
Connected Jira, Confluence, API contracts, and repository context before generating reviewable engineering changes.
Tool layer + human approval
A2TRACEABLE DELIVERY
API Verification Workflow
Correlated 398 APIs with 235 work records to preserve source traceability across analysis, implementation planning, and verification.
398 APIs / 235 work records
A3RETRIEVAL / KNOWLEDGE
Enterprise Knowledge RAG
Implemented OCR, embeddings, citations, and separated knowledge boundaries, then moved the validated capability into a usable retrieval service.
Evidence-led retrieval
A4CLOUD / EDGE
AI Service Operation
Operated internal AI and backend services with Linux/SSH routines, log review, storage and resource checks, and service-health follow-up.
Cloudflare / Docker / Edge
03 / WORKING METHOD
Speed with
visible judgment.
AI makes the loop faster. Engineering makes the result trustworthy. My workflow keeps both visible: context first, focused build, evidence, review, operation.
“The best tool workflow is the one another engineer can understand, review, and use.”
01Inspect
Understand the existing system, contracts, data, and operational constraints.
02Specify
Turn an unclear opportunity into a focused, testable product slice.
03Build
Use AI-assisted development to move quickly while keeping changes reviewable.
04Operate
Validate with working evidence, document the boundary, and hand off a useful service.
04 / AI-ASSISTED ACTIVITY
A tool layer
with mileage.
This is not a claim about a tool in isolation. It is a visible record of sustained AI-assisted engineering practice: context shaping, implementation, review, and delivery.
05 / NEXT CONVERSATION