Serdar Gündoğdu
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AI practitioner · Industrial systems

Industrial intelligence,
built for the real world.

I'm Serdar Gündoğdu—an engineer and AI practitioner who turns complex industrial data into usable products, dependable machine learning systems, and faster decisions.

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Application map · 06 live destinations

One portfolio, six focused applications.

The three-letter code is the permanent key between each application, repository, and aserdargun.com subdomain.

CodeApplicationRepositoryAddress
stkStackfolioA private workspace for tracking digital investments, memberships, and recurring costs.stk-aserdargun-comstk.aserdargun.com
aiaAI Ecosystem AtlasAn evidence-backed research console for comparing AI products and developer ecosystems.aia-aserdargun-comaia.aserdargun.com
llmLLM Runtime & Serving AtlasA source-backed field guide to LLM runtime and serving solutions across seven architectural layers.llm-aserdargun-comllm.aserdargun.com
uslUnsloth Studio LearningA bilingual learning atlas for Unsloth, LoRA, QLoRA, dataset engineering, and evaluation.usl-aserdargun-comusl.aserdargun.com
gpuGPU Kernel Engineering — Kernel AtlasA bilingual 12-week atlas for CUDA, Triton, GPU memory, profiling, and kernel optimization.gpu-aserdargun-comgpu.aserdargun.com
cldCloud Provider Cost ComparisonA source-backed decision tool comparing pre-tax USD costs for cloud services available from Türkiye.cld-aserdargun-comcld.aserdargun.com

Career journey

One path, many engineering lenses.

I began my journey with the fundamentals of mechanics. Industrial engineering taught me to see systems through a multidisciplinary lens, while a master's degree in materials and manufacturing strengthened my research-led approach. As a production engineer and manager, I worked with lean flows, quality, Six Sigma, and leadership. I later combined that industrial foundation with data science, software, and cloud engineering to build AI products. Today I deliver projects with Claude and Codex as engineering partners while developing my skills in GPU kernel engineering.

Reading direction · 09 → 01 This roadmap begins with my current focus and moves backward toward the engineering foundations that made it possible. Scroll from GPU kernel engineering through AI, data, production, and education.
  1. 09

    Now · Primary focus

    GPU Kernel Engineer — studying

    Developing the next layer of my engineering practice through GPU architecture, CUDA, Triton, profiling, and kernel optimization. In parallel, I am studying LoRA, QLoRA, dataset engineering, and evidence-aware evaluation through the Unsloth Studio Learning Atlas.

    • CUDA
    • Triton
    • GPU kernels
    • LoRA / QLoRA
    • In progress
    Explore Kernel Atlas Explore Unsloth Studio Atlas
  2. 08

    AI-native delivery

    AI Practitioner

    Uses Claude and Codex as engineering partners to research, build, review, and ship numerous projects while keeping decisions and accountability human-led.

    • Claude
    • Codex
    • AI delivery
  3. 07

    Product engineering

    Full-Stack AI Engineer

    Connected models to complete products through frontend, backend, APIs, deployment, and cloud engineering.

    • Frontend
    • Backend
    • Cloud
  4. 06

    Data

    Data Scientist

    Moved from process evidence to models, algorithms, data pipelines, and analytical software.

    • Machine learning
    • Python
    • C
    • SQL
  5. 05

    Leadership

    Production Manager

    Led teams and processes with a focus on quality, Six Sigma, repeatability, and continuous improvement.

    • Leadership
    • Six Sigma
    • Quality
  6. 04

    Operations

    Production Engineer

    Translated engineering discipline into stable production flow, value stream mapping, and Lean improvement.

    • Value stream mapping
    • Lean
    • Production flow
  7. 03

    Graduate study

    M.Sc. in Materials and Manufacturing

    Strengthened an evidence-driven approach through materials, manufacturing, and academic research.

    • Academic approach
    • Research
  8. 02

    Education

    Industrial Engineering

    Learned to connect people, processes, technology, and decisions as one operating system.

    • Multidisciplinary
    • Systems thinking
  9. 01

    Education

    Mechanical Engineering

    Built a foundation in mechanics, physical systems, and engineering problem solving.

    • Mechanics
    • Engineering fundamentals

From physical systems

to governed intelligence

to decisions people can trust

How I work

Engineering discipline. AI-native pace.

I combine industrial context, statistical thinking, and modern AI development workflows to close the gap between an idea and a system people can actually use.

01

Frame the decision

Start with the operator, engineer, or business decision—not the model. Define the signal, constraint, and useful action.

02

Build the full loop

Connect data, analysis, APIs, interface, and feedback so the intelligence survives outside a notebook.

03

Make it operable

Design for traceability, human review, deployment constraints, and the realities of industrial environments.

Predictive analyticsTime seriesIndustrial data platformsApplied machine learningAI-assisted software delivery

About

I sit at the intersection of engineering, data, and product delivery.

My background spans statistics, mathematics, mechanical and industrial engineering. That mix helps me see AI as a complete operating system: the data foundation, the model, the interface, the deployment path, and the people making the final decision.

I use AI coding partners such as Codex, Claude Code, and Gemini as part of a disciplined development workflow—accelerating research and implementation while keeping architecture, validation, and accountability human-led.

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Have an industrial AI problem worth solving?

Let's build something useful.