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.
Application map · live destinations
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. | aia-aserdargun-com | aia.aserdargun.com |
llm | LLM Runtime & Serving AtlasA source-backed field guide to LLM runtime and serving solutions across seven architectural layers. | llm-aserdargun-com | llm.aserdargun.com |
usl | Unsloth Studio LearningA bilingual learning atlas for Unsloth, LoRA, QLoRA, dataset engineering, and evaluation. | usl-aserdargun-com | usl.aserdargun.com |
gpu | GPU Kernel Engineering — Kernel AtlasA bilingual 12-week atlas for CUDA, Triton, GPU memory, profiling, and kernel optimization. | gpu-aserdargun-com | gpu.aserdargun.com |
cld | Cloud Provider Cost ComparisonA source-backed decision tool comparing pre-tax USD costs for cloud services available from Türkiye. | cld-aserdargun-com | cld.aserdargun.com |
Career journey
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 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 five applications form one learning loop, and every stop starts with a question: orient in the atlas, build through three parallel tracks — hardware, serving, training — and converge on AI infrastructure. Evidence from every track flows back into the atlas.
Study order · how the tracks build on each other
aiaLiving map · never “done”gpuFoundationllmMain projectuslFrom running to changing modelscldProduction, lastOrient
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.comBuild · 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.comllm“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.comusl“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.comgpu → 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.comReturn
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
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.
Have an industrial AI problem worth solving?
Let's build something useful.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.