New opportunity

M01 - AI Engineer

FPT Asia Pacific Pte Ltd · Singapore

About this role

Overview: As an AI Engineer, you will use Generative AI techniques complemented with data science modelling to develop models and products that support a range of divisions across investment promotion, industry development and corporate functions. You will partner with stakeholders to identify business challenges, translate user needs into technical requirements, and deliver AI and Machine Learning solutions. Responsibilities: Collaborate with business users to understand priorities; propose and develop solutions using data science and Generative AI; build and deploy AI applications and models, including an in-house agentic platform; perform data wrangling, cleaning, and feature engineering; develop backend APIs for model integration; build frontend interfaces; implement security vulnerability fixes; and research emerging AI techniques such as predictive modelling, fraud detection, and text analytics. Requirements: Bachelor's Degree in Computer Science, Computer Engineering, Machine Learning, Data Science, or related fields. Technical proficiency includes cloud platforms (AWS preferred), LLM concepts (RAG, embeddings, vector databases), AI agent frameworks (LangChain, LangGraph), web development (FastAPI, Flask, Express.js, React, Vue.js, TypeScript), containerization (Docker, Kubernetes), and BI tools (Tableau, PowerBI). Candidates must be proficient in SQL, Git, and modern programming languages like TypeScript and C#. Strong presentation and communication skills are required to explain technical concepts to non-technical audiences.

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

Skills for this role

Generative AIData ScienceMachine LearningAWSLLMRAGVector DatabasesAI Agent FrameworksLangChainLangGraphDeepAgentsFastAPIFlaskExpress.jsRESTful APIReactVue.jsHTML/CSSTypeScriptDockerKubernetesPrompt EngineeringGitSQLC#TableauQlikMS PowerBIMicrostrategy

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

Full-Stack AI Product Development
The role requires end-to-end ownership, from data wrangling and backend API development to building frontend interfaces. You must demonstrate the ability to integrate Generative AI models into functional, user-facing applications using frameworks like FastAPI, React, and TypeScript.
Agentic System Architecture
A core priority is contributing to an in-house agentic platform. You must be prepared to apply advanced LLM concepts such as RAG, vector databases, and multi-agent orchestration using tools like LangChain or LangGraph to solve complex business problems.
Stakeholder Translation and Communication
You will partner directly with non-technical business users to identify challenges and translate them into technical requirements. Success depends on your ability to explain complex AI concepts and project outcomes clearly to stakeholders across various corporate functions.

Likely challenges to think through

Balancing Rapid Prototyping with Production Security
The role involves both researching emerging AI techniques and implementing security vulnerability fixes. The challenge lies in maintaining the agility required for Generative AI experimentation while ensuring the resulting applications meet enterprise-grade security and deployment standards in Kubernetes.
Managing Context and Prompt Optimization
The requirement for harness and context engineering suggests a need for high precision in model performance. You may face difficulty in optimizing prompts and managing token windows effectively to ensure reliable outputs, requiring a disciplined approach to testing and iteration.

Practice your answers

Technical practice

RAG Architecture and Vector Databases

What to coverExplain the end-to-end flow of a Retrieval-Augmented Generation (RAG) system. Discuss the trade-offs in chunking strategies, embedding models, and how you would select a vector database based on scalability and query performance requirements.

AI Agent Frameworks (LangChain/LangGraph)

What to coverDescribe how you would design a multi-agent system using LangGraph to handle a complex task like automated document analysis. Focus on state management, tool calling, and how to ensure reliability in agentic workflows.

Full-Stack AI Application Deployment

What to coverHow do you approach the integration of a Python-based AI backend (e.g., FastAPI) with a frontend (e.g., React/TypeScript)? Discuss your strategy for containerizing these services using Docker and orchestrating them in a Kubernetes environment.

Prompt Engineering and Context Optimization

What to coverWhat techniques do you use for prompt optimization and harness engineering to improve LLM output quality? Explain how you manage context windows and token limits to balance performance and cost.

Data Wrangling and Feature Engineering

What to coverDescribe your process for cleaning and preparing unstructured data for machine learning models. Suggest how you would handle missing values or feature selection for predictive modelling tasks like fraud detection.

Security in AI Applications

What to coverWhat are the common security vulnerabilities in LLM-integrated applications, such as prompt injection or data leakage? Explain how you would implement defensive measures and security fixes within your development lifecycle.

Cloud Infrastructure (AWS)

What to coverHow would you leverage AWS services to host a production-grade AI application? Discuss the use of managed services for model deployment, storage, and monitoring to ensure high availability.

BI Integration and Data Visualization

What to coverWhen building AI products, how do you integrate model outputs with BI tools like PowerBI or Tableau? Explain how you ensure data consistency and provide actionable insights to non-technical stakeholders.

Behavioural practice

Translating Business Needs to Technical Requirements

What to coverDescribe a time you worked with a non-technical stakeholder to define a project scope. How did you translate their business challenge into a concrete AI or data science solution?

Communicating Technical Concepts

What to coverProvide an example of how you explained a complex technical trade-off (e.g., model latency vs. accuracy) to a non-technical audience. What approach did you take to ensure they understood the impact on the business?

Handling Ambiguity in Research

What to coverTell me about a time you had to research an emerging AI technique to solve a specific problem. How did you evaluate whether the technology was suitable for adoption within your organization?

Collaborative Problem Solving

What to coverDescribe a situation where you had to collaborate with a cross-functional team to deliver an AI application. How did you manage conflicting priorities or technical blockers during the development process?

Sharpen your knowledge

Topics to prepare

Retrieval-Augmented Generation (RAG) Architectures
Review the end-to-end RAG pipeline, specifically focusing on chunking strategies, embedding models, and vector database indexing. Exercise: Design a system architecture diagram for a document-based Q&A bot that handles large context windows and explain how you would manage token limits. This is relevant as the role requires building AI applications using these specific LLM concepts.
Agentic Workflow Design
Study multi-agent system patterns and state management in frameworks like LangGraph. Exercise: Create a conceptual workflow for an agent that performs multi-step research, including error handling and tool-use loops. This is directly applicable to the requirement of building an in-house agentic platform.

Skills to revise

Full-Stack AI Application Development
Practice integrating a Python-based backend (FastAPI) with a frontend (React/TypeScript) to serve model inferences. Exercise: Build a simple web interface that allows a user to input a prompt, sends it to a backend API, and displays the model response. This is essential for the role's responsibility of building end-to-end AI applications.
Containerization and Deployment
Practice creating Dockerfiles for Python and Node.js applications and defining Kubernetes deployment manifests. Exercise: Containerize a small API service and write a basic Kubernetes deployment YAML file to manage its lifecycle. This is required for deploying production-grade applications as stated in the job description.
Prompt and Context Engineering
Refine techniques for optimizing model outputs through system prompts and context injection. Exercise: Take a complex business requirement and draft a structured prompt that uses few-shot prompting to ensure consistent output formats. This is relevant to the requirement for harness and context engineering skills.

Your preparation checklist

Prepare a RAG-based project walkthrough
Develop a concise demonstration of a RAG application you have built, highlighting your choice of vector database, embedding strategy, and how you managed context windows or token limits to ensure accuracy.
Demonstrate AI agent framework proficiency
Prepare to discuss a project where you utilized LangChain or LangGraph to build an agentic workflow. Explain the logic behind your agent's decision-making process and how you handled multi-agent interactions.
Create a full-stack technical summary
Document a project where you integrated a backend API (FastAPI or Flask) with a frontend (React or Vue.js) and deployed it using Docker or Kubernetes. Be ready to explain your containerization workflow and API design choices.
Refine your technical communication skills
Practice explaining a complex AI concept, such as prompt optimization or anomaly detection, to a non-technical stakeholder. Prepare a 2-minute pitch that focuses on business value rather than just technical implementation.
Formulate strategic questions for the interview
Prepare questions regarding the current in-house agentic platform, the specific business divisions you will support, and how the team balances research into emerging techniques with the delivery of production-grade applications.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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