New opportunity

AI Software Engineer

Codex Solutions · Singapore

About this role

The AI Software Engineer joins an Agile, cross-functional product delivery team building and maintaining mission-critical digital solutions in Singapore. The role spans the product lifecycle, from understanding user needs and designing solutions through development, deployment and production operations. Engineers use AI development tools and agentic workflows to improve delivery and quality.

Responsibilities

Deliver functional, scalable features from the product backlog; build user-facing and backend capabilities that address operational problems and translate business requirements into software.
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 traditional software engineering in secure, maintainable applications.
Contribute to architecture and technical design; develop cloud-native applications on Kubernetes; implement CI/CD and GitOps workflows; and improve reliability, scalability and observability.
Write maintainable, well-tested code with attention to security, governance and operational readiness. Support production systems and troubleshoot issues.
Collaborate with product managers, designers and business stakeholders on discovery and delivery. Take end-to-end ownership and contribute to engineering standards and continuous improvement.

Qualifications

A degree in Computer Science, Information Systems, Software Engineering or a related discipline, plus experience developing applications with modern programming languages and an understanding of version control, testing and system design.
Familiarity with cloud-native development, AI technologies, LLMs, agent orchestration or retrieval systems; experience with Python, modern web frameworks, FastAPI, React or similar technologies, Kubernetes, containerized applications, CI/CD, Git-based workflows and production systems.
Strong problem-solving and analytical skills, adaptability and willingness to learn. The posting specifies 1–3 years of experience; fresh graduates with strong fundamentals, internship experience or relevant project work may also be considered.

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

Skills for this role

Artificial IntelligenceLarge Language ModelsAgent orchestrationRetrieval systemsAI-assisted developmentPythonFastAPIReactKubernetesContainerizationCI/CDGitOpsGitCloud-native developmentWeb developmentSystem designSoftware testingVersion controlProblem solvingCollaborationSecurityObservability

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

Deliver useful features end to end
The posting calls for translating user needs and business requirements into scalable, production-ready frontend and backend features. Engineers work with product managers, designers and stakeholders from discovery through operation.
Build AI into maintainable products
The role involves integrating language models and agentic workflows, and evaluating emerging AI technologies for operational use cases. The posting emphasizes combining AI with traditional software engineering securely and maintainably.
Use AI to improve engineering delivery
AI-assisted development is described as a core way of working, with an expectation to use modern tools and agentic workflows to improve productivity and quality. First-year success includes improving efficiency through automation and AI-assisted development.
Develop and deploy cloud-native applications
Engineers develop solutions deployed on Kubernetes, implement CI/CD and GitOps workflows, and contribute to architecture and technical design.
Keep production systems dependable
The role calls for well-tested code, security, governance and operational readiness, alongside improvements to reliability, scalability and observability and troubleshooting in production.

Likely challenges to think through

Choosing where AI adds value
Preparation hypothesis: an operational use case may benefit from an LLM or agentic workflow, but added complexity could outweigh its value. Prepare to explain how you would assess the user need, compare an AI approach with conventional software, and evaluate whether the result is useful and maintainable.
Balancing AI-assisted speed with code quality
Preparation hypothesis: faster development with AI tools may increase the need to check correctness, security and maintainability. Prepare an example of how you would review, test and refine AI-assisted work before deployment.
Managing broad ownership without losing reliability
Preparation hypothesis: working across discovery, application code, Kubernetes deployment and production support creates competing demands on delivery time. Prepare to discuss how you would scope a feature, automate its release, make it observable and respond to issues after launch.

Practice your answers

Technical practice

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

What to coverSuggested preparation: Explain how you would clarify user needs and business requirements, then divide the work into deliverable backlog items. Cover design, implementation, testing and operational readiness, with attention to whether the feature solves the original problem.

How would you design a user-facing feature backed by an API?

What to coverSuggested preparation: Describe the responsibilities of the interface and backend, their API contract, and how you would handle errors and changing requirements. Python, FastAPI and React are technologies named in the posting, but focus on design choices rather than assuming a prescribed stack.

How would you integrate a language-model capability into an existing application?

What to coverSuggested preparation: Separate the model interaction from core application logic and explain how you would test its behaviour and handle failures. Address maintainability and security alongside the value it provides to users.

When would you use retrieval or an agentic workflow?

What to coverSuggested preparation: Compare each approach with a simpler application or model interaction for a specific operational use case. Discuss what information or actions it needs, how you would limit unintended behaviour, and how you would evaluate the result.

How would you evaluate an emerging AI technology for an operational use case?

What to coverSuggested preparation: Define the user problem and measurable criteria before choosing a technology. Compare usefulness, reliability, security and maintenance effort with a non-AI alternative where appropriate.

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

What to coverSuggested preparation: Explain the application configuration and deployment concerns that affect reliability and scalability. Describe how you would verify that a change runs as intended without assuming a particular cluster setup.

How would you use CI/CD and GitOps to deliver a change safely?

What to coverSuggested preparation: Walk through a Git-based change from review and automated checks to deployment. Explain how you would detect a failed release and restore a working state, while distinguishing the desired deployment state from the automation that applies it.

How would you test code developed with AI assistance?

What to coverSuggested preparation: Explain how you would review generated code and choose tests for expected behaviour, edge cases and integration points. AI-assisted engineering is part of the role, but accountability for correctness and maintainability remains with the engineer.

What security and governance concerns would you check in an AI-powered feature?

What to coverSuggested preparation: Consider what data the feature receives or exposes, who may use it, and how its behaviour can be reviewed. Explain how those checks would influence the design and testing rather than treating them as a final release step.

How would you troubleshoot a production issue in a cloud-native service?

What to coverSuggested preparation: Describe how you would establish the impact, use available observations to narrow the cause, and test a fix or mitigation. Include how you would verify recovery and identify a reliability or observability improvement afterward.

Behavioural practice

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

What to coverSuggested preparation: Use a real example to describe the situation, whom you consulted, and what you did to turn the need into a workable solution. State the result and how you learned whether it addressed the user's problem.

Tell me about a time you owned work beyond your initial task.

What to coverSuggested preparation: Describe the situation, the responsibilities you took from design toward delivery or operation, and how you coordinated any handoffs. Give the result without overstating your individual contribution.

Tell me about a time you worked through a difficult software issue.

What to coverSuggested preparation: Set out the impact, your investigation and the actions you took with others where relevant. Explain the result and what you changed or learned to make a similar issue easier to handle.

Tell me about a time you had to learn an unfamiliar technology quickly.

What to coverSuggested preparation: Explain why it was needed, how you assessed and applied it, and how you checked your understanding. Describe the outcome and any trade-off you identified rather than presenting novelty as a benefit on its own.

Tell me about a time you improved a team's engineering practice.

What to coverSuggested preparation: Describe the quality, delivery or operational problem, the improvement you proposed, and how you worked with others to adopt it. Explain the observable result, including any limitations.

Sharpen your knowledge

Topics to prepare

AI capabilities in software products
The role calls for integrating language models and agentic workflows, with familiarity in retrieval systems. Preparation suggestion: sketch when a retrieval-based or agent-based approach would help an operational use case, and explain how you would evaluate its value and limitations.
End-to-end system design
Engineers contribute to architecture and own work from user needs through production. Preparation suggestion: diagram a user-facing feature, its backend, deployment path and failure points; explain trade-offs in scalability and maintainability.
Cloud-native delivery
The posting names containerised applications, Kubernetes, CI/CD and GitOps. Preparation suggestion: trace how a code change moves through build, testing, deployment and rollback, and explain where Kubernetes and GitOps fit.
Production quality and security
The role emphasizes well-tested code, security, governance, reliability and observability. Preparation suggestion: review a sample feature design and identify tests, security risks, operational signals and likely troubleshooting steps.
Product discovery and prioritization
Engineers translate business requirements into backlog features and collaborate with product managers, designers and stakeholders. Preparation suggestion: turn a stated operational problem into a small feature proposal with user needs, acceptance criteria and open questions.

Skills to revise

Build a small full-stack feature
The posting names Python, FastAPI, React or similar technologies, and user-facing and backend development. Preparation suggestion: implement a small interface backed by an API, then explain how it addresses a stated user need.
Prototype an AI-powered capability
Building AI features with language models and agentic workflows is part of the role. Preparation suggestion: add one bounded AI capability to a small application, test representative inputs and explain how you would handle unreliable outputs.
Deploy a containerised application
The posting specifies containerised applications and Kubernetes. Preparation suggestion: package a small application, deploy it to Kubernetes and check its status and application output when startup fails.
Practice Git-based delivery
The role specifies Git workflows, CI/CD and GitOps practices. Preparation suggestion: make a small change through version control, run automated checks and describe how the deployment configuration would be updated and reviewed.
Test and troubleshoot a feature
Engineers write well-tested code, support production systems and improve observability. Preparation suggestion: add tests to a feature, reproduce a failure and use its available operational signals to narrow down the cause.
Use AI development tools critically
The page says engineers use AI development tools and agentic workflows to improve delivery and quality. Preparation suggestion: use an AI tool to draft or review a small code change, then independently verify its behavior, tests and security implications.

Your preparation checklist

Choose an end-to-end project walkthrough
The role emphasizes ownership from user needs through production operations. If available, prepare a project or internship example showing the problem, your contribution, design choices, delivery, and what happened after release.
Practice a product design exercise
The posting calls for user-facing and backend features that solve operational problems. As interview practice, sketch a small feature: clarify user needs, define an API and interface, and explain trade-offs in scalability and maintainability.
Prepare an AI integration proposal
The role involves LLMs, agentic workflows and potentially retrieval systems. Sketch an operational use case, why AI adds value, how it fits into a conventional application, and how you would evaluate output quality and manage security risks.
Gather evidence of engineering fundamentals
Select available code or project examples that demonstrate version control, testing, system design and modern web development. Be ready to explain specific choices; the posting names Python, FastAPI and React or similar technologies.
Walk through deployment and troubleshooting
The posting names containers, Kubernetes, CI/CD, GitOps, observability and production support. Prepare an example you have worked on, or clearly label a proposed workflow, covering deployment, monitoring, failure diagnosis and recovery.
Rehearse collaboration and AI-assisted development examples
Prepare a concise account of turning stakeholder needs into software with product or design partners, if you have one. Also explain how you have used AI development tools—or would use them—while reviewing, testing and taking responsibility for the result.
Write questions for the employer
Ask how this team selects operational AI use cases, divides end-to-end ownership, assesses production readiness and improves engineering standards. These questions follow the posting without assuming a particular team process.

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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