Our story
Built for practitioners who want to go deeper, not faster
Garuda Tech was founded in Bangkok to give working professionals in Thailand a clear, honest way to develop AI engineering skills alongside their existing commitments.
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Where Garuda Tech came from
Garuda Tech began with a straightforward observation: a lot of the AI learning material available online assumes either that you're starting from scratch, or that you have unlimited time and a specific academic background. Neither of those descriptions fits most of the working professionals in Bangkok who want to move their practice forward.
The school takes its name from the garuda — the mythological figure that appears across Southeast Asian cultures as a patient, deliberate presence. That character is what we try to bring to how we teach: calm, structured, unhurried. We're not interested in speed-running through concepts, and we don't think learners should be either.
We started with the Software Engineering for AI course in early 2023, after noticing that the gap most practitioners needed to close wasn't about algorithms — it was about the surrounding infrastructure. Version control, project structure, dependency management, testing: the kind of work that keeps a project maintainable over months rather than days. That course has since been joined by the MLOps track and the Architecture Programme, which together cover the full arc from a working script to a documented, deployable, reviewable system.
Everything we teach is delivered by people who spend their working week on exactly these problems. We don't use a permanent faculty model. Instead, practitioners who have built production systems bring their current experience into the sessions. That keeps the curriculum grounded in what's actually required, rather than what was required three years ago.
The people involved
Core team
Siriporn Phimonrat
Programme Director
Siriporn has spent twelve years building data and ML infrastructure for financial services firms across Southeast Asia. She oversees the curriculum design and manages relationships with the practitioners who deliver the sessions.
Kritsana Wongchai
Lead Instructor — MLOps
Kritsana works as an ML engineer at a Bangkok-based logistics company. He leads the MLOps and Production Track, drawing directly on the deployment and observability challenges he encounters in his day-to-day work.
Anchisa Teerawat
Learner Experience
Anchisa coordinates the learner journey from initial enquiry through to programme completion. She manages scheduling, supports learners who have questions during a module, and gathers the feedback that shapes how we improve each cohort.
How we work
Standards we hold ourselves to
Curriculum review
Each programme is reviewed before every cohort to ensure the tools, patterns, and practices covered are still the ones learners will actually encounter in production environments.
Data privacy
We handle learner information carefully. Enrolment data is used only for running the programme. We don't share personal data with third parties for commercial purposes. Full detail in our Privacy Policy.
Cohort feedback
Every cohort closes with a structured feedback session. The responses are reviewed by the programme team and translated into concrete adjustments before the next cohort begins.
Practitioner-only instruction
We do not employ instructors who work exclusively in education. Sessions are delivered by practitioners who hold current roles in the industry, so the examples and challenges they bring are real ones.
Limited cohort size
We keep cohorts small enough for small-group sessions to be genuinely conversational. This is a deliberate constraint — we'd rather turn people away than compromise the format that makes the sessions useful.
Clear enrolment terms
Pricing, scope, and expectations are communicated in plain language before you commit. We issue VAT invoices for corporate enrolments and can discuss payment arrangements during the enrolment conversation.
Our position
What we believe about technical education
Good technical education is specific. It doesn't deal in abstractions when a concrete implementation exercise would serve the learner better. It doesn't make claims about outcomes it can't support. And it doesn't treat the learner's existing context — their codebase, their team, their work schedule — as an obstacle to be managed around.
AI development work in 2025 sits at a junction between several disciplines: software engineering, data engineering, applied research, and operational practice. Most learners arrive with depth in one of those areas and a clear need to build capacity in another. Our programmes are designed with that starting point in mind. The Software Engineering course is for practitioners who can write code but want to make it more maintainable. The MLOps track is for those who have a working model and want to move it into a stable production setting. The Architecture Programme is for people who are making or want to make design decisions at a system level.
We work in English because the primary literature and tooling in this space is in English. We work in Bangkok because that's where our team is based and where many of our learners live and work. The combination of online delivery and occasional in-person office hours reflects the reality of how working professionals in Thailand structure their time.
Work with us
Ready to talk through the options?
Send us an enquiry and we'll get back to you within one working day to discuss which programme fits your situation.
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