YK
03 / YK
Application / data / architecture / infrastructure

Engineering is not
the backstory. It is the method.

Fifteen-plus years across application development, enterprise integration, data, infrastructure and technical delivery—still actively practiced.

FOLLOW THE STORYSTART WITH 01
05 domains15+ yearsAI lab / local evaluation
SYSTEMS BLUEPRINT / LIVE DEPENDENCIES
ACTIVE DOMAIN / 01 Application engineering05 DOMAINS · ONE CONNECTED PRACTICE
Engineering surface
05working domainsAcross the stack15+years in systemsCareer from 2006AIindependent labPrivate / self-directed
SYSTEM MAP / 01

A connected practice,
not a tool list.

SYSTEMSPRACTICEAPPLICATIONDATAARCHITECTUREINFRASTRUCTUREEXTENDED PRACTICE

Each domain is useful on its own. The value appears in the handoffs between them: data shaped for an application, an interface carried by an integration, an infrastructure choice that can survive production.

FOLLOW THE HANDOFFSOpen the domain ledger ↘See the operating toolkit ↗
DOMAIN LEDGER / 02
ACTIVE DOMAIN / 01

Application engineering

C# / .NET · Python · JavaScript / TypeScript · React · Next.js · Swift

EARLY / DEPTH

Before digital-library leadership,
systems had to work.

2014—18 / NASEEJ

Senior Technical Integration Engineer

Led three staff and owned customer delivery across library portals, APIs, payments, identity, databases, servers, RFID and production support for major government and library clients.

2013—14 / SOFT COMPASS

System Analyst / .NET Developer

Requirements, ASP.NET and VB.NET development, SQL Server, reporting, systems analysis, testing and quality assurance.

2011—13 / ALMATIN

Computer & Software Engineer

Real-time energy monitoring, redundant fiber networks, SCADA servers, SQL reporting and Windows Server infrastructure.

2006—10 / FOUNDATIONS

Technical work & training

Computer and network maintenance, technical instruction and formal .NET developer training before the software-engineering degree.

CONNECT THE EXPERIENCESee delivery in context ↗Open the technical lens ↗

Independent AI engineering

Test the model.
Challenge the result.
Build from evidence.

Private, self-directed R&D in AI-powered applications, local model deployment, benchmarking, comparative evaluation, model selection, LoRA / QLoRA and vision-language models. This work is distinct from the official DCT AI Champion nomination.

LOCAL DEPLOYMENT→BENCHMARK→COMPARE→DECIDE
Read the technical lens ↗