About Me
Hi 👋,
Thanks for visiting.
About me
I build AI products — and I have done it from many different angles over the years. Honestly, that is still the fun part. 🤓
I work across applied AI, platform engineering, architecture, and technical leadership. Some days that means experimenting with models and datasets. Other days it means debugging production systems that suddenly decide 15 FPS should become 10 for emotional reasons. 😄
One of the AI products I helped build even won the Thailand ICT Award, which still makes me smile. 😊🏆
What I do
From Ideation to Production
One part of my work I especially enjoy is starting from ambiguity and turning ideas into something real.
Sometimes a project begins as nothing more than a conversation — an idea, a problem, or a rough direction. From there, I help shape the path forward: drafting UI flows, designing system architecture, defining technical approaches, documenting key considerations, building reusable templates, organizing tasks, and creating enough structure for teams to move confidently.
I also enjoy thinking about how knowledge should be documented and shared — how to keep teams aligned, how to communicate decisions clearly, and how to continuously improve the way engineers collaborate and learn from each other, especially as the AI era continues to change how we work. 😊
Architecture & Technical Leadership
I enjoy thinking about systems as a whole: architecture, scalability, engineering quality, and the trade-offs behind real-world systems.
Japanese manufacturing taught me built-in quality as a discipline. Startups taught me where to apply it pragmatically. Both lessons stayed with me. 😊
I care a lot about how systems are built — not just what works today, but what still works years later when everyone is tired and production is on fire.
I also care deeply about creating environments where engineers can make good technical decisions, learn through the process, and build with both quality and practicality in mind.
Over time, I realized that much of engineering leadership is not about controlling every decision — it is about creating clarity, good foundations, and shared understanding so teams can do their best work. 😊
Engineering Management
I have led engineering teams, mentored developers, and helped connect technical direction with business goals.
The part I enjoy most is technical leadership: architecture, engineering direction, problem solving, and helping people grow technically. I genuinely enjoy mentoring — partly because I can support others, and partly because I still learn a lot from the people around me.
Traditional management work like performance reviews, budget approvals, and administrative processes has never been the part that motivates me most. Many companies still blur the line between Engineering Manager and deeply technical leadership roles like Staff or Principal Engineer, so the responsibilities often overlap in practice.
Applied AI & ML
I develop and evaluate models, synthesize datasets, run experiments, and help turn research ideas into systems that survive contact with production.
Cloud Platform & Infrastructure
I design and build the platforms AI systems actually run on — across ECS, Kubernetes, observability stacks, and event-driven real-time architectures but it is enough for me to prove in my the EKS cluster, for production infrastructure, I need infra experts.
Why this blog
Over the years, younger engineers have asked me many of the same questions:
- How do you actually build AI products in the real world?
- How does the journey from idea to production actually work?
- Why does production behave differently from research?
- How do you make technical decisions when everything is a trade-off?
- Why does the exact same model suddenly run slower on another machine? 🥹
- Sometimes people simply tell me, “I have no idea what is wrong — can you help me figure it out?” 😄 and this happens quite often. Sometimes, simple questions from me help to fix their problems.
After answering these questions again and again, I realized I had been collecting stories, lessons, mistakes, and hard-earned experience for a long time.
So this blog became a place to share them.
Not polished conference-slide versions. I am way too lazy to do that. 😄
OK, the real things from me that nobody warns you about. The production gaps. The hidden trade-offs. The technical, the human, and everything messy in between. 🚑 🚑 🚑 🚑
If you are building AI products — or trying to — I hope something here saves you time, spares you a headache, or simply reminds you that you are not the only person questioning reality 😄
Welcome — and I hope you find something useful here.