Full-Stack Developer (AI-Augmented Engineering)

What matters most

• Own production features end to end

  The role is primarily product engineering: design, build, and maintain web applications across the frontend, backend, and data layer.

• Use AI assistance across the development lifecycle

  The posting expects daily use of coding assistants from specification and scaffolding through implementation, testing, and review—not just code completion.

• Ship useful LLM and agentic features

  Integrate LLM-powered features where they add user or operational value, and use an agent framework to connect LLMs with APIs, data sources, and internal tools.

• Own code quality regardless of its origin

  Critically review, debug, and refactor AI-generated code, including teammates’ contributions; write maintainable code with unit and integration tests.

• Coordinate reliable delivery

  Work with product and engineering peers on scope and API contracts, deliver under deadlines, and document designs, decisions, and AI-assisted workflows.

Likely challenges to think through

• AI speed versus code ownership

  Likely preparation challenge, not a stated employer problem: assistant output may accelerate implementation while hiding subtle errors. Prepare an example of how you checked generated code, found a mistake, and tested the correction.

• LLM value versus added complexity

  Likely preparation challenge: the role calls for LLM features only where they add value, while models have practical limitations. Prepare to explain how you would choose an appropriate use case, account for model limitations, and decide whether a lightweight workflow is sufficient.

• Cross-stack scope under deadlines

  Likely preparation challenge: changes spanning the UI, backend, and data layer must fit agreed API contracts and delivery timelines. Prepare an example of how you narrowed scope, coordinated an interface, and preserved testing and documentation.

Topics to prepare

• Full-stack application design

  Draw the data flow for a web feature from a React-style frontend through a REST backend to a relational database; explain the API contract and where validation belongs. Relevant because this is primarily an end-to-end product engineering role.

• Practical LLM behavior

  Explain how tokens and context windows constrain a feature, what embeddings represent, and two ways a model can produce unreliable output. The posting explicitly requires practical understanding of these concepts and model limitations.

• Agent workflows and tool calling

  Diagram a lightweight workflow in which an LLM selects a function, receives JSON from an API or data source, and uses the result; explain how you would handle a failed call. Agent-framework experience and tool/function-calling fundamentals are required.

• AI-assisted development and code ownership

  Walk through a specification-to-review workflow using an AI coding assistant, identifying where you supply context and independently verify its output. The role expects assistant use throughout development and ownership of every shipped line.

• Testing and API-contract decisions

  For a proposed feature, distinguish unit from integration test boundaries and describe how product and engineering peers would agree on its API contract. The posting calls for both test types, collaborative scoping and reliable delivery.

Skills to revise

• Build an end-to-end web slice

  As a revision exercise, implement a small React-style UI, a REST endpoint in Python, Go or Rust, and a relational-database read or write; trace a request across all three layers. The role builds and maintains production applications across those layers.

• Implement a bounded agent workflow

  Using one listed agent framework, connect an LLM to a function that reads an API or data source; validate the JSON response and handle an unsuccessful call. This practices the required hands-on framework work and API integration.

• Use an AI coding assistant across a task

  Give Claude Code, Cursor, GitHub Copilot or a similar assistant a clear specification and relevant code context; use it for a draft implementation and tests, then record what you changed during review. The posting calls for daily assistant use beyond snippet completion.

• Debug and test generated code

  Take an AI-generated change with a subtle incorrect assumption, reproduce the defect, refactor the code, and add unit and integration tests that would catch it. This directly exercises the stated expectation to review AI output critically and test AI-generated contributions.

• Document a feature decision

  Write a short feature scope, JSON API contract, design trade-off and account of where AI assistance was used; review the contract against a frontend call and backend response. The role requires collaboration on scope and contracts and clear documentation of decisions and AI-assisted workflows.

• Map the required experience to evidence

  The posting asks for 4+ years of professional full-stack work. Prepare a factual timeline of roles and specific frontend, backend, data-layer, REST API, relational database, and Git work you can discuss; leave gaps unclaimed.

• Rehearse an end-to-end project walkthrough

  Choose a production web application you worked on, if available. Prepare a short account of its architecture, your contributions, API contracts, testing, maintenance decisions, and how you collaborated to deliver it.

• Sketch a product-feature design

  As a practice exercise, draw a frontend-to-database flow and define a sample REST/JSON contract. Show where an LLM feature would add user or operational value, and where conventional code would be more reliable; this is preparation, not a stated interview exercise.

• Prepare an agent-framework example

  The role requires hands-on framework experience and accepts personal or academic projects. If you have one, diagram how its LLM connects to an API, data source, or tool, including tool-call inputs, outputs, failure handling, and what you implemented.

• Demonstrate ownership of AI-assisted code

  Prepare a real example, if available, of using a coding assistant across planning, implementation, testing, and review. Be ready to explain the context you supplied, an incorrect output you caught, the correction, and unit or integration tests that supported the shipped code.

• Practice explaining LLM fundamentals

  Write brief explanations of tokens, context windows, embeddings, prompting, model limitations, and tool/function calling. Tie each to a concrete implementation or testing choice rather than treating the terms as a checklist.

• Draft questions for Forcepoint

  Ask how the team decides when LLM features add value, reviews AI-generated contributions, defines API contracts, and documents AI-assisted workflows. These questions follow the advertised responsibilities without assuming a particular team process.