Serdar Gündoğdu

AI engineer · Industrial systems

Industrial intelligence,
built for the real world.

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

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Living system

Journey

Past

Explore the engineering experience behind the systems I build.

 Active

Now

A dated view of where my attention and work are going now.

 Horizon

Future

The long-term direction is open humanoid engineering in the physical world.

 Published

Knowledge

Explore published decisions, research notes, and the sources behind them.

 Working outputs

Applications

Accumulated knowledge becomes focused applications, labs, and long-term work.

Career journey

One path, many engineering lenses.

I started with mechanics; industrial engineering and a materials-and-manufacturing master's taught me to see systems through a multidisciplinary, research-led lens. As a production engineer and manager, I worked with lean flows, quality, Six Sigma, and leadership. I then combined that industrial foundation with data science, software, and cloud engineering to build AI products — today I design and deliver AI systems with Claude and Codex as engineering partners.

Reading direction · 08 → 01 This roadmap runs backward from my current AI focus through data, production, and education to the engineering foundations that made it possible.
  1. 08

    Now · Primary focus

    Portrait associated with the AI Engineer career stage
    DIGITAL WORLDTurning intelligence into working systems

    AI Engineer

    I design and build AI systems end to end, connecting model development, dataset engineering, evaluation, software, and cloud delivery. My current work also includes model adaptation, agentic workflows, and evidence-aware evaluation.

    • AI systems
    • Model engineering
    • Dataset engineering
    • Evaluation
    • Product delivery
  2. 07

    Product engineering

    Portrait associated with the Full-Stack AI Engineer career stage
    DIGITAL WORLDFrom models to products

    Full-Stack AI Engineer

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

    • Frontend
    • Backend
    • Cloud
  3. 06

    Data

    Portrait associated with the Data Scientist career stage
    DIGITAL WORLDFrom data to models

    Data Scientist

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

    • Machine learning
    • Python
    • C
    • SQL
  4. 05

    Leadership

    Portrait associated with the Production Manager career stage
    PHYSICAL WORLDFrom operations to data

    Production Manager

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

    • Leadership
    • Six Sigma
    • Quality
  5. 04

    Operations

    Portrait associated with the Production Engineer career stage
    PHYSICAL WORLDMaking production measurable

    Production Engineer

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

    • Value stream mapping
    • Lean
    • Production flow
  6. 03

    Graduate study

    Portrait associated with the M.Sc. Materials and Manufacturing career stage
    PHYSICAL WORLDFrom materials to evidence

    M.Sc. in Materials and Manufacturing

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

    • Academic approach
    • Research
  7. 02

    Education

    Portrait associated with the Industrial Engineering career stage
    PHYSICAL WORLDSystems and flow

    Industrial Engineering

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

    • Multidisciplinary
    • Systems thinking
  8. 01

    Education

    Portrait associated with the Mechanical Engineering career stage
    PHYSICAL WORLDMatter and mechanics

    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.

AI Learning System is where I connect what I study with what I build. I share current projects and research so others can learn alongside me. My next direction brings world models and collective intelligence into industrial twins and, over time, open humanoid engineering.

Current work Future direction Read my writing opens in a new tabStart a conversation

Have an industrial AI problem worth solving?

Let's build something useful.

Open to remote collaborations · TR / EN