Carousell Group

Forward Deployed Engineering Intern (AI Adoption Pod)

Singapore · On-site · Internship

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About the role

Description from the employer’s posting. Check the employer’s page for the current version.

EmployerCarousell Group
LocationSingapore
Work styleOn-site
Employment typeInternship
Employer posted19 days ago

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

Show full description

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

Preferred availability: Oct 2026-Jun 2027 full-time > Oct–Dec 2026 part-time + Jan-Jun 2027 full-time > Jan-Jun 2027 full-time only.

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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