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 isnt 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 Youll Do

• Sit with internal teams (e.g. Data Product Marketing Sales Ops People Finance) to understand what theyre actually trying to solve youll 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 cant: 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 youre the one who fixes it

• Stay with a workflow past its built the job isnt done until the team can see its working and the outcome is measurable

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

Qualifications :

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 youre 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 thats about to change anyway) as well as when to

• What you hand over doesnt become next months incident cost security and maintenance tradeoffs were thought through not just shipped

Additional Information :

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Remote Work :

No

Employment Type :

Intern