AI Application Developer

What matters most

• Build usable AI applications

  The role builds applications for workflow automation, team assistance and customer experiences across FoodHub’s restaurant technology platform. The posting emphasizes Python, FastAPI, Pydantic, LLM APIs, function calling and structured outputs.

• Deliver real-time voice experiences

  Voice solutions are a stated responsibility, including conversational agents, phone-based ordering and real-time customer support. The posting specifically calls for understanding audio pipelines, turn-taking and latency optimization.

• Connect agents to real systems

  The developer will design single- and multi-agent systems with appropriate human oversight and connect them to company data and services so they can answer questions and take actions using real information.

• Translate needs before building

  FoodHub expects close work with non-technical stakeholders to understand problems, gather requirements and turn business needs into technical solutions.

• Improve deployed systems

  The posting calls for cloud-native production experience, observability and reliability practices, plus monitoring outputs and optimizing reliability, latency and cost.

Likely challenges to think through

• Balancing voice speed and response quality

  Preparation hypothesis, not a confirmed FoodHub problem: phone and real-time conversations may make delays noticeable, while faster responses can be less dependable. Prepare to explain how you would measure end-to-end latency and test turn-taking and response quality together.

• Choosing how much agent autonomy to use

  Preparation hypothesis: an agent that can act through company services creates a trade-off between useful automation and human control. Prepare to explain when a simple prompt chain is sufficient, when an agent is warranted, and where you would require oversight before actions.

• Keeping production AI practical

  Preparation hypothesis: improving output quality can increase latency or cost. Prepare to describe how you would evaluate outputs, observe failures and use those measurements to prioritize changes rather than relying on a successful demo.

Topics to prepare

• LLM application design

  Revise prompt engineering, function calling and structured outputs by sketching a restaurant-order question flow: identify what the model can answer directly, when it needs company data and what a valid response must contain. These are explicit application-building expectations.

• Agent architecture and oversight

  Compare a prompt chain, a single agent and a multi-agent design for handling an order issue. Mark where a person should approve an action. The posting explicitly calls for choosing practical architectures and providing human oversight.

• Real-time voice flow

  Draw the stages of a phone-ordering conversation from speech recognition through response generation to speech synthesis; annotate turn-taking and likely latency points. Voice agents, phone ordering and latency optimization are explicit priorities.

• Agent interoperability

  Explain, using a simple diagram, how an agent could reach a company data source or service through a tool, and where MCP or A2A would fit. Familiarity with these protocols and connections is requested.

• Production AI quality

  Draft a small review checklist for incorrect answers, failed actions, response time and cost, then describe what you would monitor after release. The role includes evaluating outputs and improving reliability, latency and cost.

• Requirements before implementation

  Practice turning a non-technical request for customer support automation into a problem statement, clarifying questions and documented system behavior. The posting emphasizes stakeholder communication and understanding the problem before coding.

Skills to revise

• Python API implementation

  Build a small FastAPI endpoint with Pydantic request and response models for an order-status query; test valid and invalid inputs. Strong Python, FastAPI and Pydantic skills are explicitly sought.

• LLM tool calling

  Prototype an LLM API interaction that calls a mock order-lookup function and returns a validated structured answer; test missing or conflicting order data. The posting requests experience with LLM APIs, function calling and structured outputs.

• Voice-agent prototyping

  Using one of the listed STT/TTS services and one listed voice frameworks, rehearse a short ordering dialogue and inspect interruptions, turn boundaries and response delay. Experience with these components and real-time audio behavior is explicitly sought.

• Connecting agents to services

  Implement a small mock data-access tool and trace what an agent sends, receives and does when the tool fails; explain how that connection relates to MCP or A2A. The role calls for connecting agents to real company data and services, without requiring access to FoodHub systems for practice.

• Observability and evaluation

  Add logs and a few repeatable test cases to a prototype, recording response time, tool failures and output quality; propose one improvement based on the results. This reflects the stated production, observability and monitoring responsibilities.

• Stakeholder explanation

  Give a two-minute plain-language explanation of a proposed voice-ordering flow, including its limits and points needing human review, then write a brief behavior note for teammates. Clear communication, requirements gathering and documentation are explicitly requested.

• Prepare a Python application walkthrough

  Bring a concise walkthrough of an application you built with Python, FastAPI and Pydantic, if available. Show request validation, API design and a technical decision you made; these are stated skills for the role.

• Explain an LLM workflow end to end

  Prepare a diagram or example showing prompts, function calls and structured outputs, including how errors are handled. The posting asks for LLM API and agentic-framework experience.

• Sketch a voice-ordering design

  As a preparation exercise, diagram a phone-based ordering agent from speech input through turn-taking, tool use and spoken response. Be ready to discuss latency and interruptions; the posting identifies these voice-AI concerns.

• Compare simple and multi-agent approaches

  Write a short design comparison for a restaurant-platform task: when a prompt chain is sufficient, when an agent is justified, and where human approval belongs. Include how tools and data would be connected, drawing on the role’s A2A and MCP requirements.

• Gather production and evaluation evidence

  Prepare one example, if available, of deploying or maintaining a cloud-native application. Note what you monitored, how you evaluated output quality, and how you investigated reliability, latency or cost; these are explicit responsibilities.

• Practice translating a business need

  Draft a brief requirements-gathering conversation with a non-technical stakeholder, followed by a one-page technical proposal. Use a hypothetical workflow-automation need rather than assuming FoodHub has a specific project planned.

• Bring focused questions for FoodHub

  Ask which voice or agent use cases are priorities, what data and services an AI application may access, where human oversight is required, and how deployed-system quality is measured. These questions follow from the advertised responsibilities.