New opportunity

Senior AI Application Developer

FoodHub · Chennai, India

About this role

FoodHub is seeking a full-time Senior AI Application Developer in Chennai, India, to build AI-powered applications for its restaurant technology platform. The role focuses on automating workflows, assisting teams and improving customer experiences.

Responsibilities

Work with non-technical stakeholders to understand business problems and translate requirements into technical solutions.
Design AI prompts and workflows that deliver consistent, reliable results. Connect AI systems to company data so they can answer questions and take actions using real information.
Monitor deployed systems, track performance, resolve issues and improve reliability. Evaluate emerging AI tools for potential adoption.

Requirements

The listing states 3 years of work experience. Strong Python skills, including FastAPI and Pydantic, and experience building applications with AI or LLM APIs, SDKs and frameworks are sought.
Production experience deploying and maintaining cloud-native applications; practical problem-solving, clear communication, and careful testing and validation of AI outputs.

Preferred

TypeScript skills are a plus.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonFastAPIPydanticTypeScriptLLM APIsAI SDKsAI frameworksPrompt engineeringCloud-native applicationsTestingStakeholder communication

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Turn business problems into applications
The role calls for working with non-technical stakeholders before coding, then building AI applications that automate workflows, assist teams, or improve customer experiences across FoodHub’s restaurant technology platform.
Build reliable AI workflows
The posting emphasizes effective prompts and workflows with consistent results, backed by careful testing and validation of AI outputs. Prepare to explain how you would evaluate an application’s behavior, not just demonstrate a prompt.
Ground AI in company data
AI systems are expected to answer questions and take actions using real company information. Prepare to discuss how an application would access relevant data and check that its responses and actions are supported by that data.
Own production applications
FoodHub seeks Python skills, including FastAPI and Pydantic, experience with AI/LLM APIs, SDKs, and frameworks, and experience deploying and maintaining cloud-native applications. The duties also include monitoring performance, fixing issues, and improving reliability.

Likely challenges to think through

Balancing speed with dependable outputs
A likely difficulty is shipping useful AI workflows while ensuring their results stay consistent and validated. Prepare an example of how you would test outputs, investigate failures, and decide whether a workflow is ready to deploy.
Using real data for answers and actions
Connecting AI to company data may be harder when an application must take actions rather than only answer questions. Prepare to explain how you would verify the information used and handle uncertain or unsupported results before an action proceeds.
Choosing improvements without chasing novelty
The role combines maintaining deployed systems with evaluating emerging AI tools. A likely trade-off is whether a new tool offers enough benefit to justify adoption; prepare to compare it against observed performance and reliability needs.

Practice your answers

Technical practice

How would you scope an AI application for a restaurant-platform workflow?

What to coverThe role involves automating workflows, assisting teams and improving customer experiences. Suggested preparation: choose a hypothetical use case, then explain how you would define its users, success measures and boundaries before choosing an AI approach.

How would you structure a Python API for an AI-powered application?

What to coverFoodHub names Python, FastAPI and Pydantic. Suggested preparation: explain request and response validation, separation of API and AI logic, and how you would handle invalid inputs or upstream errors.

How would you integrate an LLM API or SDK into an application?

What to coverExperience building applications with AI or LLM APIs, SDKs and frameworks is sought. Suggested preparation: discuss interface design, error handling and trade-offs between using a framework and calling an API directly.

How would you make prompts and AI workflows more consistent?

What to coverThe role calls for prompts and workflows that produce reliable results. Suggested preparation: describe clear instructions, constrained outputs and repeatable tests, including what you would change when results vary.

How would you connect an AI system to company data for answering questions?

What to coverThe posting says AI systems should answer using real company information. Suggested preparation: explain how you would select relevant data, handle missing or conflicting information and check that answers reflect the data provided.

How would you design an AI workflow that can take actions using company information?

What to coverTaking actions based on real information is an advertised duty. Suggested preparation: discuss when an action should proceed, how inputs and outcomes should be validated, and the trade-off between automation and safeguards.

How would you test and validate AI outputs before and after release?

What to coverFoodHub explicitly calls for careful testing and validation of AI outputs. Suggested preparation: describe representative test cases, criteria for acceptable results and how you would investigate failures.

What would you consider when deploying and maintaining a cloud-native AI application?

What to coverProduction experience with deployed, maintained cloud-native applications is sought. Suggested preparation: explain how you would manage configuration, dependencies and failures while keeping the application maintainable.

How would you troubleshoot a deployed AI system whose results have become less reliable?

What to coverThe role includes tracking performance, fixing issues and improving reliability. Suggested preparation: describe which observations you would compare, how you would isolate the cause and how you would verify a fix.

How would you decide whether an emerging AI tool is worth adopting?

What to coverEvaluating new AI tools is part of the role. Suggested preparation: propose a small comparison against the current approach using reliability, usefulness, integration effort and operational costs.

Behavioural practice

Tell me about a time you clarified an unclear business problem with a non-technical stakeholder.

What to coverThe role emphasizes understanding problems before coding. Suggested preparation: use a real situation, your questions and actions, and the resulting change in requirements or solution.

Tell me about a time you explained a technical trade-off to a non-technical audience.

What to coverClear communication is an advertised skill. Suggested preparation: describe the decision, how you made the alternatives understandable and what resulted.

Tell me about a complex application problem you broke into practical steps.

What to coverFoodHub seeks a practical problem-solving mindset. Suggested preparation: give the situation, how you prioritized and tested possible steps, and the outcome.

Tell me about a production issue you investigated and resolved.

What to coverThe role involves maintaining deployed systems and improving reliability. Suggested preparation: describe the issue’s impact, your diagnostic and corrective actions, and how you checked the result.

Tell me about a time testing revealed that an AI output was not dependable.

What to coverCareful validation of AI outputs is explicitly sought. Suggested preparation: explain the situation, how you identified and addressed the problem, and what the result showed.

Sharpen your knowledge

Topics to prepare

Business-to-technical translation
The role requires understanding problems with non-technical stakeholders before coding. For revision, take a hypothetical restaurant-platform request, list clarifying questions, then draft measurable acceptance criteria and a proposed AI workflow.
Prompt and workflow reliability
The posting calls for prompts and workflows that produce consistent, reliable results. Sketch a multi-step workflow and identify where ambiguous inputs, incorrect outputs or failed steps need handling.
Company-data grounding and actions
The role connects AI systems to company data to answer questions and take actions using real information. For revision, diagram how a hypothetical request would obtain relevant data, check an answer against it and verify an action before execution; this is a preparation exercise, not a stated architecture.
Production reliability and tool evaluation
The posting requires cloud-native deployment and maintenance, performance tracking, issue resolution and evaluation of emerging AI tools. Compare two hypothetical design options using reliability, operational effort and usefulness to the business problem as criteria.

Skills to revise

Build a Python API with FastAPI and Pydantic
Strong Python, FastAPI and Pydantic skills are explicitly sought. Practice implementing a small endpoint with validated request and response models, then test malformed inputs and error responses.
Integrate an AI or LLM API
The posting seeks experience building applications with AI/LLM APIs, SDKs and frameworks. Practice calling an AI API from a small application, handling unsuccessful calls and inspecting the returned output; no particular provider is specified.
Test and validate AI outputs
Careful testing and validation are explicit requirements. Create a compact set of representative and edge-case inputs for a hypothetical restaurant-platform task, record expected behavior and review inconsistent or unsupported outputs.
Diagnose a deployed application issue
The role involves monitoring deployed systems, tracking performance, fixing issues and improving reliability. Rehearse a hypothetical incident: identify a failing request pattern, isolate a likely cause, propose a fix and define how to check whether it worked.
Explain a solution to a non-technical stakeholder
Clear communication and requirements gathering are explicitly requested. Practice a brief explanation of an AI workflow's purpose, limitations and validation plan, then ask questions to resolve one unclear business requirement.

Your preparation checklist

Prepare a relevant project walkthrough
Choose an AI application you built and explain the problem, your contribution, the AI/LLM APIs or frameworks used, and what happened after deployment. If it was not in restaurant technology, explain the transferable decisions without implying it was.
Rehearse a stakeholder discovery conversation
Prepare questions for a non-technical stakeholder, then practice turning their answers into a problem statement, requirements and a proposed solution. The role explicitly involves understanding business needs before coding.
Sketch a Python application design
Outline a small AI-powered workflow using FastAPI and Pydantic, including request validation, an AI integration and response handling. Be ready to explain the design choices rather than claim the employer uses this exact architecture.
Show how you test AI results
Prepare a prompt and workflow example with a small test set, expected behaviors, failure cases and a way to check consistency. Explain how you would revise the workflow when outputs are unreliable.
Plan a data-connected AI exercise
Sketch how an application could answer questions using company data and, where appropriate, take an action based on it. Identify what data and permissions you would need to clarify; the listing does not specify an integration.
Gather production and tool-evaluation evidence
Prepare an example of deploying and maintaining a cloud-native application, including how you tracked performance, diagnosed an issue and improved reliability. Also prepare criteria you would use to assess whether a new AI tool is worth adopting.
Bring targeted questions for FoodHub
Ask which restaurant-platform workflows are the initial priorities, what company data AI applications may access, how reliability is measured, and how technical teams work with non-technical stakeholders.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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