Forward Deployed Engineering Intern (AI Adoption Pod)

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

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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