AI engineer · Industrial systems
I'm Serdar Gündoğdu—an AI engineer who turns complex industrial data into usable products, dependable machine learning systems, and faster decisions.
The work behind me is a traceable journey from physical systems to AI.
ActiveA dated view of where my attention and work are going now.
HorizonThe long-term direction is open humanoid engineering in the physical world.
PublishedOnly explicitly approved public memory snapshots appear here.
Working outputsAccumulated knowledge becomes focused applications, labs, and long-term work.
Career journey
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.
Now · Primary focus
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.
Product engineering
Connected models to complete products through frontend, backend, APIs, deployment, and cloud engineering.
Data
Moved from process evidence to models, algorithms, data pipelines, and analytical software.
Leadership
Led teams and processes with a focus on quality, Six Sigma, repeatability, and continuous improvement.
Operations
Translated engineering discipline into stable production flow, value stream mapping, and Lean improvement.
Graduate study
Strengthened an evidence-driven approach through materials, manufacturing, and academic research.
Education
Learned to connect people, processes, technology, and decisions as one operating system.
Education
Built a foundation in mechanics, physical systems, and engineering problem solving.
Learning system · AI Ecosystem Atlas
The six applications form one learning loop: AIA orients the system; GPU provides the compute foundation; USL adapts models; and LLM connects both through runtime and serving. GPU and LLM inform the local-compute path in LCL, while LLM informs the cloud-services path in CLD. Evidence from both decision surfaces flows back into the atlas.
Learning path · foundations first, deployment decisions in parallel
aiaLiving map · never “done”aiaLiving map · never “done”opens in a new tabgpuFoundationgpuFoundationopens in a new tabllmMain projectllmMain projectopens in a new tabuslFrom running to changing modelsuslFrom running to changing modelsopens in a new tablclLocal deployment decisionlclLocal deployment decisionopens in a new tabcldCloud deployment decisioncldCloud deployment decisionopens in a new tabOrient
aia“What exists?”
Start with the map of the territory. Compare AI products and developer ecosystems with evidence before investing time in any track. The atlas is a living index, not a project to finish — every run through the loop updates it.
ecosystem models training inference runtime hardware cloud
aia.aserdargun.com opens in a new tabBuild · three parallel tracks
gpu“How does compute work?”
GPU Kernel Engineering — Kernel Atlas
Learn the machine the models run on: CUDA, Triton, GPU memory, profiling, and kernel optimization across a bilingual 12-week atlas.
CPU vs GPU GPU architecture VRAM memory bandwidth CUDA / Tensor cores FP32 / FP16 / BF16 / FP8 / INT8 / INT4 matrix multiplication CUDA kernels FlashAttention KV cache quantization multi-GPU tensor parallelism
gpu.aserdargun.com opens in a new tabllm“How do models run?”
LLM Runtime & Serving Atlas
Learn to ship models: runtime and serving solutions classified across seven architectural layers. Hardware and training both feed this track.
model architecture precision memory calculator runtime inference engine serving API benchmark
Ollama llama.cpp vLLM SGLang TensorRT-LLM Transformers MLX
llm.aserdargun.com opens in a new tabusl“How do models learn/change?”
Unsloth Studio Learning
Learn to change models, not just run them: Unsloth, LoRA, QLoRA, dataset engineering, and evaluation in a bilingual learning atlas.
pretrained model dataset tokenization LoRA QLoRA SFT DPO GRPO evaluation merged model LLM runtime
usl.aserdargun.com opens in a new tabgpu → llm hardware decides what you can serve usl → llm the models you train become what you serve llm → gpu inference pressure exposes the hardware limits
Converge
cld“How do I operate this at scale?”
Cloud Provider Cost Comparison
Turn the stack into decisions: compare pre-tax USD costs of cloud services available from Türkiye and choose where the whole system runs. Cloud comes last on purpose — once you know what you deploy, infrastructure stops being abstract.
model vLLM Docker GPU instance cloud GPU load balancer autoscaling API
cld.aserdargun.com opens in a new tabReturn
The loop closes when what you learned returns to the atlas — the map you started with becomes the record of what you can now build, and the next pass through the loop starts deeper.
From physical systems
to governed intelligence
to decisions people can trust
Application map · live destinations
Six core learning applications, one lab, and one long-term horizon. The three-letter code is the permanent key between each application, repository, and aserdargun.com subdomain.
| Code | Application | Repository | Address |
|---|---|---|---|
aia | AI Ecosystem AtlasAn evidence-backed research console for comparing AI products and developer ecosystems.Current | aia-aserdargun-com opens in a new tab | aia.aserdargun.com opens in a new tab |
llm | LLM Runtime & Serving AtlasA source-backed field guide to LLM runtime and serving solutions across seven architectural layers.Current | llm-aserdargun-com opens in a new tab | llm.aserdargun.com opens in a new tab |
usl | Unsloth Studio LearningA bilingual learning atlas for Unsloth, LoRA, QLoRA, dataset engineering, and evaluation.Current | usl-aserdargun-com opens in a new tab | usl.aserdargun.com opens in a new tab |
gpu | GPU Kernel Engineering — Kernel AtlasA bilingual 12-week atlas for CUDA, Triton, GPU memory, profiling, and kernel optimization.Current | gpu-aserdargun-com opens in a new tab | gpu.aserdargun.com opens in a new tab |
cld | Cloud Provider Cost ComparisonA source-backed decision tool comparing pre-tax USD costs for cloud services available from Türkiye.Current | cld-aserdargun-com opens in a new tab | cld.aserdargun.com opens in a new tab |
lcl | Local Compute LabA source-backed decision workbench matching open-weight model requirements with NVIDIA, AMD, and Apple devices for a local AI lab.Current | lcl-aserdargun-com opens in a new tab | lcl.aserdargun.com opens in a new tab |
itl | Industrial Twin LabAn isolated experimentation environment for digital twins, industrial AI, simulation, and evidence-driven machine knowledge — where the AI stack meets the physical asset.Current | itl-aserdargun-com opens in a new tab | itl.aserdargun.com opens in a new tab |
eng | Open Humanoid EngineeringThe long-term horizon for embodied, open humanoid engineering — what the atlas, hardware, runtime, training, cloud, and industrial twin lab all serve.Horizon · in design | eng-aserdargun-com opens in a new tab | eng.aserdargun.com opens in a new tab |
How I work
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.
Start with the operator, engineer, or business decision—not the model. Define the signal, constraint, and useful action.
Connect data, analysis, APIs, interface, and feedback so the intelligence survives outside a notebook.
Design for traceability, human review, deployment constraints, and the realities of industrial environments.
About
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.
Open to remote collaborations · TR / EN
Investment · depth beats order
The study order is AIA → GPU → LLM → USL → CLD; the depth priority is LLM first. Right now the center of gravity is the GPU + LLM pair — where the hardware meets the runtime.