New opportunity

AI Software Engineer

Codex Solutions · Singapore

About this role

The AI Software Engineer joins an Agile, cross-functional product delivery team in Singapore to build and maintain mission-critical digital solutions. The role combines end-to-end software ownership with AI product engineering, including language-model capabilities and agentic workflows. The posting describes a permanent position suited to engineers with 1–3 years of experience; fresh graduates with strong fundamentals, internship experience or relevant project work may also be considered.

Responsibilities

  • Design, develop, test and maintain production-grade user-facing and backend applications. Translate business requirements and user needs into scalable features, and contribute to architecture and technical design.
  • Build and integrate AI-powered capabilities using modern language models and agentic workflows. Evaluate emerging AI technologies for operational use cases and combine AI with conventional software in secure, maintainable applications.
  • Develop cloud-native solutions on Kubernetes. Use CI/CD and GitOps to automate delivery, and improve reliability, scalability, observability and maintainability.
  • Write well-tested code, consider security and governance, support production operations and troubleshoot issues. Work with product managers, designers and business stakeholders on discovery and delivery.

Requirements

  • A degree in Computer Science, Information Systems, Software Engineering or a related discipline; experience developing applications in modern programming languages; and knowledge of version control, testing and system design.
  • Familiarity with cloud-native practices, AI technologies, LLMs, agent orchestration or retrieval systems. The posting also seeks experience with Python, modern web frameworks such as FastAPI or React, Kubernetes and containerised applications, CI/CD, Git-based workflows, and building and operating production systems.
  • Strong problem-solving and analytical skills, willingness to learn, and comfort working across the technology stack.

First-year goals include delivering features used in production, growing the product, improving engineering efficiency through automation and AI-assisted development, contributing to engineering standards, and demonstrating ownership through production operation.

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

Skills for this role

PythonFastAPIReactKubernetesGitCI/CDGitOpsLarge Language ModelsAgent orchestrationRetrieval systemsAI-assisted developmentCloud-native developmentContainerizationAgileSoftware testingSystem designArchitecture designVersion controlObservabilitySecurityWeb developmentProblem solving

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Get interview-ready for this role

A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • Own features from need to production

    The role spans discovery, design, development, deployment and operations. First-year success includes production-ready features used by real users and ownership through production operation.

  • Build useful AI capabilities

    The posting calls for integrating language models and agentic workflows into applications, evaluating emerging AI technologies for operational use cases, and combining AI with conventional software securely and maintainably.

  • Deliver reliable cloud-native software

    The engineer will develop Kubernetes-deployed solutions, use CI/CD and GitOps to automate delivery, and improve reliability, scalability and observability.

  • Maintain quality and operational readiness

    Responsibilities include well-tested, maintainable code, security and governance considerations, and troubleshooting production issues when required.

  • Collaborate and improve engineering practice

    The role works with product managers, designers and business stakeholders to understand user needs. First-year goals also include improving efficiency through automation and AI-assisted development and contributing to engineering standards.

Likely challenges to think through

  • Balancing broad ownership with delivery

    Preparation hypothesis, not a reported employer problem: covering user needs, frontend and backend work, deployment and operations could compete with feature deadlines. Prepare one example of how you scoped work, made design trade-offs and carried a feature through release.

  • Making AI behavior dependable

    Preparation hypothesis: language-model or agentic features can be harder to test and control than conventional application logic, creating a trade-off between capability and security or maintainability. Prepare to explain how you would evaluate an operational use case, test outputs and set boundaries around AI behavior.

  • Automating without weakening production safeguards

    Preparation hypothesis: faster CI/CD, GitOps and AI-assisted development may increase the need for review, testing and observability. Prepare an example or design approach showing how automated delivery can retain checks and support troubleshooting.

03

Practice your answers

Technical practice

How would you turn an operational need into a production-ready feature?

What to coverPreparation suggestion: Explain how you would clarify the user need, define acceptance criteria, and design the user-facing and backend components. Discuss scalability and maintainability trade-offs appropriate to the feature.

How would you connect a web interface to a Python backend?

What to coverPreparation suggestion: Use FastAPI and React as examples named in the posting, without assuming they are mandatory for every project. Cover API contracts, validation, error handling, and how you would test the interaction.

How would you decide whether a language model adds value to an operational workflow?

What to coverPreparation suggestion: Compare an AI approach with conventional software against user value, reliability, cost, and operational complexity. Explain what evidence you would gather before putting the capability into production.

How would you design an agentic workflow that remains controllable?

What to coverPreparation suggestion: Describe how you would constrain actions, handle failures, and keep a clear boundary between model output and application logic. Address security and maintainability rather than assuming autonomous behaviour is always useful.

When would you use retrieval alongside a language model?

What to coverPreparation suggestion: Explain how retrieval could supply relevant information and how you would assess whether it improves answer quality. Discuss limitations such as irrelevant results, stale information, and the need to check outputs.

How would you test a feature that combines conventional code and AI output?

What to coverPreparation suggestion: Separate deterministic software tests from checks of variable model behaviour. Explain how you would test failure paths and use evaluation results to decide whether the feature is ready for users.

What would you consider when deploying a containerised application on Kubernetes?

What to coverPreparation suggestion: Cover application configuration, resource needs, health checks, and safe rollout or recovery. Relate those choices to the posting’s emphasis on reliability, scalability, and production operation.

How would you structure a Git-based CI/CD and GitOps delivery workflow?

What to coverPreparation suggestion: Describe how code changes would be reviewed, tested, built, and promoted through an automated workflow. Explain how you would detect a bad release and restore a working deployment.

How would you troubleshoot a production issue in a user-facing feature?

What to coverPreparation suggestion: Start with the user impact and available observations, then narrow the fault across the interface, backend, and deployment. Explain how observability and a verified fix would help prevent recurrence.

How would you account for security and governance in an AI-powered application?

What to coverPreparation suggestion: Discuss access controls, handling of sensitive information, and review of model-generated content or actions. Explain the trade-off between adding useful AI capability and keeping the application secure and operable.

Behavioural practice

Tell me about a time you clarified an unclear user or business need.

What to coverPreparation suggestion: Choose a real work, internship, or project example and describe the situation, what you did to understand the need, and the result. Focus on how your actions changed the feature or delivery decision.

Describe a feature you took from initial idea through delivery or operation.

What to coverPreparation suggestion: Give the situation, your specific actions across development and release, and the result for users or the product. Be clear about what you owned and where others contributed.

Tell me about a technical decision you worked through with people in different roles.

What to coverPreparation suggestion: Set out the situation, the differing priorities, and how you worked with others to reach a decision. Explain the outcome and any trade-off you accepted.

Describe a time you investigated a difficult software issue.

What to coverPreparation suggestion: Explain the situation and impact, the steps you took to isolate and address the problem, and the result. Include what you verified or changed afterward to reduce repeat issues.

Tell me about a time you learned a new approach or improved an engineering workflow.

What to coverPreparation suggestion: Use a real example to show the situation, how you evaluated and applied the approach, and its result. If relevant, discuss how you checked the quality of automation or AI-assisted work rather than assuming it was beneficial.

02

Sharpen your knowledge

Topics to prepare

  • End-to-end product and system design

    Suggested exercise: Sketch a user-facing feature from a business need through interface, backend, deployment and production support; explain scalability and maintenance trade-offs. Posting relevance: Engineers translate requirements into features, contribute to architecture and own products through operation.

  • LLM application patterns

    Suggested exercise: Compare a direct language-model call, retrieval-based response and agentic workflow for a hypothetical operational use case; identify where each could fail. Posting relevance: The role integrates language models and agentic workflows, and seeks familiarity with retrieval systems.

  • Cloud-native delivery and reliability

    Suggested exercise: Draw the path from a containerised application to Kubernetes deployment, then explain how you would investigate a failing deployment or reliability issue. Posting relevance: Kubernetes, containerised applications, observability and production operations are stated.

  • CI/CD, GitOps and Git workflows

    Suggested exercise: Outline how a code change moves through version control, tests, deployment and a GitOps-managed configuration change. Posting relevance: The role calls for Git-based workflows and automated build, deployment and operational processes.

  • Quality, security and operational readiness

    Suggested exercise: Review a proposed AI-enabled feature and list the tests, security considerations, governance questions and operational checks you would raise before release. Posting relevance: The posting emphasises well-tested code and secure, maintainable production systems.

Skills to revise

  • Build a small full-stack feature

    Suggested exercise: Implement a Python backend endpoint with FastAPI and a simple React interface that uses it; handle invalid input and describe the design choices. Posting relevance: Python, FastAPI or similar frameworks, React or similar technologies, and user-facing and backend development are named.

  • Integrate and test an AI capability

    Suggested exercise: Add a small language-model capability to a sample application, then test expected responses, failure handling and where human review may be needed. Posting relevance: The role combines AI capabilities with conventional software in secure, maintainable applications.

  • Containerise and deploy an application

    Suggested exercise: Containerise a sample application, deploy it to a practice Kubernetes environment and diagnose a deliberately broken deployment. Posting relevance: The posting seeks experience with containerised applications, Kubernetes and production troubleshooting.

  • Automate delivery with Git

    Suggested exercise: Put a sample application under Git, add automated tests to a CI/CD pipeline and practise reviewing a deployment configuration change. Posting relevance: Git-based workflows, CI/CD and GitOps are explicitly listed.

  • Test and troubleshoot a production-style issue

    Suggested exercise: Write focused tests for a feature, then use application logs and an observed failure to isolate a defect and propose a fix. Posting relevance: Well-tested code, observability, reliability and production support are part of the role.

  • Verify AI-assisted development output

    Suggested exercise: Use an AI development tool to draft a small code change, then inspect the diff, run tests and document corrections before accepting it. Posting relevance: The posting expects effective use of AI development tools and agentic workflows to improve delivery quality.

04

Your preparation checklist

  • Prepare an end-to-end project walkthrough

    The role covers user-facing and backend development through production operation. If you have a relevant project, prepare a short walkthrough of the user need, your contribution, design choices, tests, deployment and outcome.

  • Sketch an AI feature for an operational use case

    The posting calls for language-model capabilities and agentic workflows. As a practice exercise, outline a feature that combines AI with conventional software; explain how you would evaluate its usefulness, security and maintainability.

  • Practice a system-design discussion

    Be ready to turn a business requirement into a scalable feature. Prepare a simple architecture diagram and explain the API, user interface, data flow and trade-offs; Python, FastAPI and React are relevant technologies named in the posting.

  • Map out a cloud-native delivery path

    The role names containers, Kubernetes, CI/CD and GitOps. Prepare a diagram showing how a code change could be tested, reviewed, built and deployed, plus what you would monitor after release.

  • Gather evidence of quality and ownership

    If available, select examples of Git-based work, testing, troubleshooting and production support. For each, note the problem, your actions and the result; the posting emphasizes well-tested code and operational readiness.

  • Prepare a collaboration and AI-assisted development example

    The team works with product managers, designers and business stakeholders, and uses AI development tools. Prepare an example, if you have one, of clarifying user needs or using an AI tool while checking the quality of its output.

  • Write focused questions for the interview

    Ask how engineers share end-to-end ownership, which operational use cases are priorities for AI, and what production readiness and engineering efficiency mean for this team. These questions follow the role’s stated responsibilities and first-year goals.

AI-generated from the job information · Prepared Sep 30, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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