Forward Deployed Engineering Intern (AI Adoption Pod)

Carousell Group is the leading multi-category platform for secondhand in Greater Southeast Asia on a mission to make secondhand the first choice. Founded in August 2012 in Singapore, the Group has a leading presence in seven markets under the brands Carousell, Carousell Media Group, Cho Tot, Laku6, LuxLexicon, Mudah.my, OneShift, REFASH and Revo Financial, serving tens of millions of monthly active users. Carousell is backed by leading investors including Telenor Group, Rakuten Ventures, Naver, STIC Investments, 500 Global and Peak XV Partners (formerly known as Sequoia Capital India).

As a team of passionate individuals working together to solve meaningful problems, there is so much more for you to discover in a career with Carousell. Our culture is made up of hiring, developing, and promoting people who embody our values of HEART, which is an acronym for Humility, Empathy, Accountability, Relentlessly resourceful and Teamwork. Together as an organisation, we make magic happen.

About the Pod

Carousell Group is building a small pod of engineers to help internal, non-engineering teams figure out the right tools, workflows, and agents to multiply their impact. This isn't about basic build support — most teams can already put together a simple AI workflow on their own. The pod exists for the harder calls: what should run on Claude versus another tool, how to weigh cost and latency tradeoffs, how to architect the link between a front-end and the underlying infrastructure so it holds up under real use, and what it takes to keep something secure and maintainable after launch.

What You'll Do

• Sit with internal teams (e.g. Data, Product, Marketing, Sales Ops, People, Finance) to understand what they're actually trying to solve — you'll rarely get a fixed spec, and will need to sharpen fuzzy problems through conversation and rapid prototyping

• Build and iterate on AI-powered skills, workflows, and agents for real, non-technical users

• Make the calls a non-technical builder can't: what to deploy and where, how to weigh cost against speed and latency, how to architect the connection between a front-end tool and the underlying infrastructure

• Debug in production — when something breaks for a real user, you're the one who fixes it

• Stay with a workflow past "it's built" — the job isn't done until the team can see it's working and the outcome is measurable

• Feed patterns back to the pod: what's reusable across teams, what needs a different approach each time

Role Specific Competencies

Must

• Strong fundamentals in software engineering — writes correct, working code independently rather than completing a guided assignment

• Strong working knowledge of GenAI primitives — prompting, context engineering, MCP tool/function calling — and has personally built a non-trivial working output with modern AI/LLM tooling (e.g. Claude), beyond using it as a chat assistant

• A track record of shipping something real end-to-end (personal project, academic project, or internship) — took an idea to a working, used piece of software

• Given an ambiguous, unscoped problem, can independently break it down and drive to a solution without a detailed spec

Should

• Basic grasp of cost/latency/security tradeoffs in system design — can reason about why one architectural choice beats another, even without production-scale experience

• Full-stack literacy — comfortable enough across front-end, backend/API, and data layer to connect them without hand-holding

• Some exposure to debugging a real failure in a running system, not only local testing

• Has experience building and deploying agentic workflows or tool-using agents, not just single-shot prompting

Nice to Have

• Has contributed to or maintained a live system other people depend on (open source, internship, or work project)

• Exposure to more than one language/stack, showing fast pickup

• Some early product sense — can explain a technical tradeoff in terms a non-engineer would follow

What Success Looks Like

• The team you're paired with can point to something measurably better because of what you built — not just "a workflow exists somewhere"

• You know when not to build something (e.g. a workflow that's about to change anyway) as well as when to

• What you hand over doesn't become next month's incident — cost, security, and maintenance tradeoffs were thought through, not just shipped

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