[Product] [SPM] Senior Product Manager, AI-Native Builder (Bangkok-based)

Title: [Product] [SPM] Senior Product Manager, AI-Native Builder (Bangkok-based) Location: Bangkok

The opportunity

This role is BANGKOK-BASED, RELOCATION PROVIDED.

AI has changed what one person can build in a week. We're looking for someone who has already made that shift. You'll find where work at Agoda is slow, manual or inconsistent, build something better yourself, and prove that it works.

This role is mostly inward-facing. Your customers are your colleagues, and your product is how Agoda works: how we go from idea to production, how teams make decisions, and how much of their day goes on work that shouldn't need a person. You'll join Product AI Labs, a small team within Product, building the tools, agents and ways of working that make Agoda faster and raise the quality of what we ship.

Your work happens in two settings:

• With your own small team, working alongside developers to turn what works into products people rely on.
• Across the whole company. One week you might be inside an engineering team's release process. The next you might be with Legal, Finance or Customer Experience, working out how AI could change the way they work.

You'll run many experiments. Some won't work, and we'll stop those quickly. The ones that do work can grow from one team to hundreds of colleagues, and sometimes to the whole company.

Like every Agoda Senior Product Manager, you're analytical, data-driven and hands-on. The difference is that your main output is working software, and your success is measured by how fast and how well Agoda builds.

This is an Individual Contributor position with high visibility to Product and Engineering leadership.

In this role, you'll get to:

• Find the opportunities yourself. Spend time with teams across the company, in Product, Engineering, Legal, Finance, Operations and beyond. Map how their work actually flows, and decide which problems are worth solving. Turn what you learn into a prioritized set of problems, not a queue of requests.
• Build the first version yourself. Use current AI models and agentic tools to prototype and ship agents, internal tools and automations. Make prototype-first the default: turn an idea into something that runs, then decide whether it deserves to become a product.
• Prove it, then scale it. Take an idea far enough on your own to show clearly whether it works. When it does, partner with Engineering to make it robust, secure and ready for everyone to rely on. When it doesn't, stop it early and move on to the next experiment.
• Make us more productive, and prove it. Define and track the measures that matter, such as cycle time, time saved, quality, and adoption that lasts. Measure outcomes, not activity.
• Raise the quality of our work. Use AI to raise the bar on specs, reviews, testing, experiment design and decision-making. Define what "good" means for every AI tool you ship, and keep watching it after launch.
• Do it safely. Work with Engineering, Security and Legal so that what you build handles data, privacy and cost responsibly.
• Spread the practice. Teach, pair, demo and write up what works. Turn one-off wins into reusable patterns and playbooks, so building with AI becomes how Agoda works, not something one team does.

What you'll need to succeed:

• A record of building, especially with AI. You use AI models and agentic tools (for example Claude Code, Cursor, Codex or similar) in your real work, every week, to build working tools, agents and prototypes, and you can show us. How you build matters less than whether it ships and works. Using AI only to summarize documents or draft text doesn't count.
• Things you've built that people use. Ideally at least one tool, workflow or product, at work or outside it, that others adopted and kept using. You can explain how you measured whether it worked.
• 3+ years of product management experience, or equivalent experience building products (for example as an engineer, data scientist, designer or founder who owned outcomes).
• A bachelor's degree in a technical or quantitative field (Computer Science, Statistics, Engineering, Mathematics or similar), or equivalent practical experience building products.
• Strong data skills. You use analytics and quantitative methods to make decisions, and you're comfortable answering your own questions with data.
• Product judgment. You can do discovery, prioritize, give a reasoned no, and tell an impressive demo apart from a real improvement.
• Judgment and accountability with AI. You define "good" before you start, check outputs critically, and own the result. You think about privacy, security and cost by default.
• Entrepreneurial, with a bias for action. You break down ambiguous problems, don't need the problem scoped for you, and drive change across teams that don't report to you.
• Excellent communication in English, written and spoken. You explain complex things clearly, concisely and with conviction, often with a demo instead of a deck.
• Learning momentum. You're visibly better at building with AI than you were six months ago, and you can tell us what you tried, dropped and changed.

It's great if you have:

• The ability to read and change code (Python, TypeScript, SQL or similar).
• A background as an engineer, data scientist or technical founder before moving into product.
• Experience with developer experience, internal platforms, or tools that improve how teams work.
• Hands-on work with agent frameworks, MCP, evals, or multi-step automations.
• Experience rolling out AI tools inside a company, including the change management that goes with it.
• Public writing, talks or open-source work on building with AI.

Show us what you've built

Alongside your CV, share one thing you've built with AI. It can be personal or professional, and you choose what to share. Tell us briefly:

1. What it is, and what problem it solves.
2. Evidence that it exists and works, such as a link, demo video, repo, live app, screenshots or a short write-up.
3. Which part you built, and how.
4. What happened as a result, such as who uses it or what changed.

Something rough that real people use tells us more than a polished demo nobody uses.

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