Full-Stack Engineer - AI Products

What matters most

• Build fast, accessible interfaces

  The role calls for performant, accessible applications in Next.js, React, and TypeScript, using modern patterns including server components. Prepare to explain UI decisions in a shipped feature.

• Deliver APIs and AI-powered features

  The posting assigns ownership of REST or GraphQL APIs and integration of LLM APIs, vector search, and AI pipelines into interfaces and backend services. Prepare an end-to-end architecture explanation; prior LLM API integration is listed as nice to have, not required.

• Own data design and query performance

  Database design, data modelling, and query optimisation are explicit responsibilities, with PostgreSQL, MongoDB, and Redis named. Prepare to explain how a data model supports product queries and how you would investigate a slow query.

• Ship from discovery through deployment

  The engineer works with clients and a solutions architect from discovery to launch, owns features across the stack, and sets up CI/CD and deployment infrastructure. Prepare a concise account of decisions and handoffs across that lifecycle.

Likely challenges to think through

• Keeping AI interactions usable

  A likely design challenge is balancing responsive interfaces with the time an LLM-backed workflow may take to return results. Prepare to discuss loading and error states; streaming via SSE or WebSockets is a nice-to-have approach, not a stated requirement.

• Choosing boundaries across the stack

  With UI, APIs, and data design under one role, a likely trade-off is where to put logic while keeping queries efficient and interfaces maintainable. Prepare to walk through one design choice and the alternatives you rejected.

• Turning client discovery into shipped features

  Direct client collaboration through launch may require balancing requested behaviour against implementation and deployment complexity. Prepare an example of clarifying a requirement, agreeing on scope, and carrying a feature through release; the posting does not specify a particular client problem.

Topics to prepare

• React rendering and TypeScript

  The posting calls for strong TypeScript and modern React patterns, including hooks and server components, in Next.js applications. Suggested revision: sketch an intelligent dashboard and explain which parts render on the server, which need client-side interaction, and how you would type their data.

• REST and GraphQL API design

  The role involves designing APIs, while the stated experience requirement is REST or GraphQL. Suggested revision: design an endpoint or query for a dashboard, then explain its request shape, errors, and how the React client consumes it.

• Data modeling and query optimization

  The posting emphasizes data modeling and query optimization and names PostgreSQL, MongoDB, and Redis in the responsibilities. Suggested revision: model dashboard records, trace a slow read, and explain which data-store and query changes you would consider and why.

• Deployment and delivery pipelines

  The role includes CI/CD and deployment infrastructure on AWS, GCP, or Vercel; familiarity with cloud platforms and pipelines is requested. Suggested revision: diagram the steps from a code change through checks, deployment, and rollback for one of those platforms.

• AI product integration

  Integrating LLM APIs, vector search, and AI pipelines is a stated responsibility; prior experience with specific LLM APIs is listed as nice to have, not required. Suggested revision: trace how a dashboard request would pass through an API, retrieval step, and LLM call, identifying latency and failure points.

Skills to revise

• Build an accessible Next.js interface

  Suggested exercise: implement a small typed React dashboard view with a hook-driven interaction, keyboard-accessible controls, and a clear loading state. This practices the posting’s Next.js, React, TypeScript, performance, and accessibility work.

• Implement an API and connect the UI

  Suggested exercise: build one REST endpoint or GraphQL query using Node.js or Python with FastAPI or Django, then consume it from the dashboard view. This mirrors the required experience building and consuming APIs without treating every listed backend option as mandatory.

• Test a data-access improvement

  Suggested exercise: create a small dashboard dataset, write a query, inspect why it may be slow, and revise the query or data model; explain any role for PostgreSQL, MongoDB, or Redis. This reflects the role’s database ownership and optimization work.

• Walk through a deployment

  Suggested exercise: prepare a CI/CD plan for the small application, including checks, configuration, deployment to AWS, GCP, or Vercel, and rollback. This practices the advertised deployment responsibility; no particular pipeline tool is specified.

• Prototype an AI-backed interaction

  Suggested exercise: outline or prototype a UI-to-backend flow that calls an LLM API and uses vector search, with a visible error state. This targets the stated integration work. Streaming via SSE or WebSockets is optional preparation because the posting labels it nice to have.

• Practice a client-to-launch handoff

  Suggested exercise: turn a mock client request for an intelligent dashboard into a brief scope, API/UI decisions, and an async launch update for a solutions architect. This reflects the stated direct client collaboration and end-to-end feature ownership; the mock request is only a preparation exercise.

• Choose a shipped-project walkthrough

  The role calls for end-to-end feature ownership. Prepare a walkthrough of something you shipped, covering your contribution from UI and API decisions through deployment, with a work link you can share.

• Prepare a frontend implementation example

  The posting emphasizes performant, accessible Next.js, React, and TypeScript applications. Outline a relevant component or feature and be ready to explain your choices around hooks, server components, accessibility, and performance.

• Sketch an API and data design

  As a practice exercise, design one REST or GraphQL endpoint for an intelligent dashboard. Show the request and response, a data model, and how you would investigate a slow query; choose a backend and database relevant to the posting rather than trying to use every listed option.

• Plan an AI-feature discussion

  Integrating LLM APIs, vector search, and AI pipelines is part of the work, while prior LLM API integration experience is listed as nice to have. Prepare either a real example or a clearly labeled hypothetical design that explains how an AI response reaches the UI; treat streaming as optional.

• Map a deployment path

  The role includes CI/CD and deployment on AWS, GCP, or Vercel. Prepare a concise diagram of a pipeline you have used, or a hypothetical one, showing how a change is tested, deployed, and checked after release.

• Rehearse client-facing ownership

  The posting describes working with clients and a solutions architect from discovery through launch. Prepare one example of clarifying requirements and handling a tradeoff, plus questions about how the delivery pod makes decisions and coordinates asynchronously.

• Assemble the requested application materials

  Prepare a CV or LinkedIn profile, a link to something you have shipped, and 2–3 lines explaining your interest in this specific role. The posting specifies the subject line “Application – Full-Stack Engineer - AI Products” for an email application.