이 공고, 이렇게 물어볼 겁니다

AI 에이전트를 실제 고객에게 배포하는 과정에서, '파일럿' 단계의 결과와 '확장된 규모(Scale)'의 제품 방향을 결정하기 위해 어떤 측정 지표(Metrics)를 사용했으며, 그 결정 과정에서 가장 큰 데이터의 불확실성은 무엇이었습니까?

🎯 실제 성과 측정 및 스케일업 경험을 확인하고, 모호한 상황에서 데이터 기반으로 의사결정하는 능력을 확인합니다.

고객과의 복잡한 대화(chaotic conversation)를 통해 얻은 인사이트를 엔지니어링 팀이 실행 가능한 명확한 제품 방향(Product Direction)으로 전환한 구체적인 사례를 제시해 주시고, 이때 AI 도구를 어떻게 활용하여 그 과정의 속도와 정확도를 높였는지 설명해 주십시오.

🎯 PM이 추구하는 '카오스 속에서 명확한 계획을 도출'하는 능력과 AI 도구 활용 능력을 확인합니다.

AI 에이전트의 가치를 정의할 때, 단순히 효율성(Efficiency)이나 비용 절감(Cost Saving)을 넘어 'Delighted'라는 감성적 가치(Feeling of being understood and cared for)를 어떻게 측정하고 제품 로드맵에 반영했는지 구체적인 사례를 들어 설명해 주십시오.

🎯 회사가 추구하는 '감성적 경험(Delight)'이라는 비즈니스 목표를 제품 지표로 전환하는 전략적 사고를 확인합니다.

귀하가 생각하는 '생산 가능한(Production-grade)' AI 에이전트가 '데모(Demo)' 수준을 넘어 실제 운영 환경에서 성공하기 위해 반드시 갖춰야 할 기술적/제품적 요구사항은 무엇이며, 이 요구사항이 기존의 엔지니어링 프로세스와 어떻게 충돌했는지, 그리고 어떻게 조율했는지 설명해 주십시오.

🎯 기술적 이해도와 엔지니어링 현실에 대한 이해를 바탕으로, 이상과 현실 사이의 균형을 잡는 협업 능력을 확인합니다.

APAC 지역 고객을 대상으로 AI 에이전트를 배포하는 과정에서 발생했던 가장 심각한 운영상의 마찰(Friction) 사례와, 이를 해결하기 위해 고객과 엔지니어링 팀 사이에서 어떤 강력한 의견을 제시하고 어떤 증거를 기반으로 설득하여 최종적으로 해결책을 도출했는지 설명해 주십시오.

🎯 복잡한 이해관계자 관리, 갈등 해결, 그리고 데이터 기반의 설득력을 확인합니다.

질문만 읽으면 컨닝이에요. 소리 내어 답해보세요 — 어디서 틀어지는지 짚어드립니다.

공고 내용

Most PMs write specs and wait for feedback. You'll be deploying AI agents with real customers across APAC, then turning what you learn into product direction that actually matters.

The Company

Sendbird is on a mission to build the AI workforce of tomorrow. For over a decade, we built the infrastructure behind conversations—chat, voice, video, messaging APIs—and became the #1 CPaaS platform for in-app communications. 4,000+ brands trust us. 7 billion messages flow through our platform every month. 300 million monthly active users.

We powered conversations for DoorDash, Match Group, Noom, Yahoo Sports, Rakuten, and thousands of others. We were good at what we did. Really good.

We also saw it early: AI would fundamentally reshape how businesses talk to customers. The infrastructure we'd spent a decade building would become commoditized. The value would move up the stack—into intelligence, into experience, into outcomes.

We had a choice: protect what we built, or reinvent ourselves.

We chose reinvention.

In December 2024, we made the full strategic pivot to AI-first customer experience. By February 2025, we'd launched our AI agent for enterprise CX—built on a decade of conversation data, now with intelligence on top. And in November 2025, we rebranded to delight.ai.

The name says it all. AI's real promise isn't efficiency or cost savings. It's giving customers back something they lost—the feeling of being truly understood and cared for. Not satisfied. Delighted.

The Product

Delight.ai is the AI concierge for customer experience. Most AI agents forget you the moment the conversation ends. Ours doesn't. Delight.ai builds memory over time, learns preferences, and connects context across every channel—chat, SMS, email, voice, WhatsApp—without losing the thread. We're building AI that makes customers feel understood, seen, and remembered.

Why Forward Deployed Product Manager

Enterprise AI isn't won in the roadmap, it's won in the field. Businesses across APAC are trying to figure out what production-grade AI agents actually look like for their customers. The gap between "pilot" and "valuable at scale" is where deals are won or lost, and where the most important product learnings live.

This role exists because we need someone who can close that gap. You'll work directly with customers to get AI agents live and deliver measurable outcomes, using AI tooling to move faster, learn faster, and build smarter. Then bring what you learn back to shape the product itself. It's one of the highest-leverage roles we're hiring for right now.

If you’d like to dive deeper, check out the podcast linked for more details! 🎧 LINK

The Role

You'll own the full arc from pilot to production for APAC customers deploying our AI Agent platform: designing agents, driving adoption, and converting real-world friction into product direction. This role sits at the intersection of product, engineering, and customer success, and it's built for someone who does their best thinking in the messy middle, with AI tools in hand.

You might be this person if:

· You're more energized by a chaotic customer conversation than a perfectly formatted PRD, and you use AI to make sense of the chaos faster.

· You can walk into an ambiguous problem, ask the right three questions, and leave with a clear plan, often with an AI-assisted artifact ready before you leave the room.

· You hold strong opinions about what makes AI agents actually useful in production, not just in demos, and you can back them up with evidence.

· You find it genuinely satisfying to translate "we need this to work better" into something engineering can build, and you use agentic tools to prototype and validate before escalating.

· You get restless when you're too far from the user. You want to see the problem, not just hear about it.

· You're comfortable saying "I don't know yet" and uncomfortable staying there for long. You reach for AI tools before asking someone else.

· The idea of frequent, sometimes last-minute travel to customer sites across APAC sounds more exciting than it does disruptive.

You need to have:

· 2 to 3 years of experience in a PM or product-facing role.

· A CS degree or technical equivalent. You understand how software is built, and you use AI coding tools and agentic CLIs to close the gap between insight and execution.

· Demonstrated ability to work directly with customers and translate messy, complex needs into clear product outcomes.

· Fluency in English, with the ability to operate across the APAC region.

What you'll actually do:

· Partner with APAC customers to understand their business context and co-design AI agents that solve real problems, using agentic tools to accelerate scoping and configuration.

· Lead deployments end-to-end, from initial scoping through pilot to production rollout, building repeatable, AI-assisted workflows along the way.

· Synthesize customer feedback into structured, actionable input for product and engineering teams, using AI to surface patte

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