New opportunity

AI Architect (Group IT)

Far East Organization · Singapore

About this role

The AI Architect (Group IT) will serve as the enterprise design authority for AI architecture at Far East Organization (FEO). The role is responsible for defining AI reference architecture, platform standards, solution patterns, and technical roadmaps to drive digital transformation across business units. The focus includes Generative AI, LLMs, AI agents, RAG, and scalable integration of AI into enterprise systems. Responsibilities: Define enterprise AI reference architectures and platform standards. Lead design for GenAI, LLM, RAG, multi-agent workflows, and predictive ML solutions. Evaluate and select AI/ML technologies, foundation models, and orchestration frameworks. Design AI-ready data foundations including vector search, knowledge graphs, and data lineage. Implement LLMOps/MLOps practices for model registry, evaluation, and observability. Ensure secure integration with enterprise systems like ERP and CRM. Establish Responsible AI and governance controls, including bias mitigation and prompt injection protection. Partner with business units to shape use cases and guide delivery teams from concept to production. Provide technical leadership and mentoring to engineers and architects. Requirements: Minimum 8 years of experience in software engineering, data engineering, cloud, or enterprise architecture, with at least 3 years of hands-on experience in production AI/ML or GenAI/LLM solutions. Strong knowledge of enterprise AI platforms (Azure OpenAI, AWS Bedrock, etc.) and RAG architectures. Proficiency in Python, SQL, and containerization technologies (Docker, Kubernetes). Experience with AI orchestration frameworks (LangChain, LlamaIndex, etc.) and MLOps/LLMOps lifecycles. Strong understanding of enterprise data architecture and AI security/governance. Degree in Computer Science, IT, Engineering, or Data Science required; Master’s degree or relevant certifications preferred. Experience with SAP or real estate/hospitality systems is an advantage.

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

Skills for this role

Generative AILarge Language ModelsRAGAI AgentsMLOpsLLMOpsAzure OpenAIAWS BedrockGoogle Vertex AISemantic KernelLangChainLlamaIndexLangGraphPythonSQLDockerKubernetesAzure DevOpsEnterprise ArchitectureCloud Architecture

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

Enterprise AI Reference Architecture
Establish standardized, scalable AI patterns and platform architectures that integrate seamlessly with existing enterprise systems like ERP and CRM. This requires balancing technical innovation with the stability and security requirements of a large organization.
Governance and Responsible AI Frameworks
Define and enforce controls for AI safety, including bias mitigation, prompt injection protection, and PII handling. You must ensure all AI deployments comply with internal policies and Singapore regulatory expectations while maintaining auditability.
LLMOps and MLOps Lifecycle Management
Implement robust operational practices for model registry, observability, and deployment. The priority is to move AI solutions from concept to production by managing model drift, latency, and quality monitoring across environments.
AI-Ready Data Foundation Design
Architect the underlying data infrastructure required for RAG and agentic workflows, including vector search, knowledge graphs, and data lineage. You must ensure data quality and access-aware retrieval are built into the architecture from the start.

Likely challenges to think through

Balancing Agility with Enterprise Rigor
The challenge lies in adopting rapidly evolving GenAI technologies while maintaining the strict governance and security standards of a large real estate organization. Prepare by demonstrating how you have managed technical debt and security risks in past AI implementations.
Cross-Functional Stakeholder Alignment
You will need to translate complex AI capabilities into business value for diverse units like hospitality and property operations. Prepare by highlighting your experience in shaping use cases and guiding non-technical stakeholders through the feasibility and ROI assessment process.
Integration with Legacy Enterprise Systems
Integrating modern AI agents and LLMs with established systems like SAP or legacy CRM platforms presents significant technical hurdles regarding data residency and API security. Prepare by discussing your approach to secure, scalable integration patterns in hybrid cloud environments.

Practice your answers

Technical practice

Designing Scalable RAG Architectures

What to coverExplain how you would design a RAG pipeline for an enterprise environment. Focus on the trade-offs between different chunking strategies, vector database selection, and the implementation of hybrid search or re-ranking to improve retrieval accuracy.

Implementing LLMOps and Model Observability

What to coverDescribe the components of a robust LLMOps lifecycle. Address how you would manage prompt versioning, monitor for model drift or latency, and implement evaluation frameworks to ensure consistent output quality in production.

AI Security and Governance Controls

What to coverHow do you protect enterprise AI systems against prompt injection and data leakage? Discuss your approach to implementing guardrails, PII masking, and ensuring compliance with regulatory standards within an AI architecture.

Orchestration Frameworks for Multi-Agent Systems

What to coverCompare the use of frameworks like LangChain, LlamaIndex, or LangGraph for building agentic workflows. Explain how you determine when a multi-agent approach is appropriate versus a simpler, single-model solution.

Integrating AI with Enterprise Systems

What to coverDiscuss the challenges of integrating AI models with legacy systems like ERP or CRM. Focus on authentication, secure API design, and ensuring that AI-driven insights respect existing role-based access controls.

Data Foundations for AI Readiness

What to coverWhat are the essential characteristics of a data platform that supports both predictive ML and GenAI? Address the importance of data lineage, metadata management, and the role of knowledge graphs in enhancing model context.

Cloud-Native AI Infrastructure

What to coverExplain how you leverage containerization (Docker/Kubernetes) and CI/CD pipelines to deploy AI models. Suggestion: Focus on how you ensure portability across different cloud providers like Azure or AWS.

Evaluating Foundation Models

What to coverWhat criteria do you use to select between proprietary foundation models (e.g., Azure OpenAI) and open-source alternatives? Discuss factors such as data residency, cost, performance, and specific use-case requirements.

Behavioural practice

Translating Business Needs to Technical Roadmaps

What to coverDescribe a time you had to align a complex technical AI initiative with business objectives. How did you communicate the value and feasibility to non-technical stakeholders to secure buy-in?

Managing Technical Debt in AI Projects

What to coverGive an example of how you balanced the need for rapid prototyping with the requirement for long-term, scalable architecture. How did you manage the transition from a successful pilot to a production-grade system?

Mentoring and Technical Leadership

What to coverProvide an example of how you have mentored junior engineers or guided a cross-functional team through a challenging architecture design. How did you ensure the team adhered to best practices?

Navigating Conflicting Stakeholder Requirements

What to coverDescribe a situation where you had to reconcile conflicting requirements between security, data governance, and business units regarding an AI implementation. How did you reach a consensus?

Sharpen your knowledge

Topics to prepare

Enterprise AI Governance and Security
The role requires establishing controls for prompt injection, bias mitigation, and PII handling. Exercise: Draft a high-level governance framework document that outlines how to implement human-in-the-loop design and auditability for an LLM-based application integrated with an ERP system.
RAG and Agentic Architecture Design
The position focuses on scalable RAG and multi-agent workflows. Exercise: Design a conceptual architecture diagram for a document intelligence system that includes chunking strategies, hybrid search, re-ranking, and knowledge graph integration to ensure source attribution.
LLMOps and MLOps Lifecycle
The role demands managing the full lifecycle from development to production. Exercise: Create a checklist for a production-ready LLMOps pipeline, specifically detailing how you would monitor for model drift, latency, and quality degradation in a real-time enterprise environment.

Skills to revise

AI Orchestration Frameworks
Proficiency in LangChain, LlamaIndex, or Semantic Kernel is required. Exercise: Build a simple Python prototype using one of these frameworks that chains a prompt to a vector database retrieval step to answer domain-specific questions.
Cloud-Native AI Deployment
The role requires containerization and cloud platform knowledge. Exercise: Write a Dockerfile for a Python-based AI service and describe how you would deploy this container to a Kubernetes cluster with integrated CI/CD pipelines using Azure DevOps or GitHub Actions.
Enterprise Data Integration
The role involves integrating AI with systems like SAP or CRM. Exercise: Map out a data pipeline strategy that ensures data lineage and quality when moving data from a legacy SQL-based enterprise system into an AI-ready vector database.

Your preparation checklist

Develop an Enterprise AI Reference Architecture
Create a high-level architectural diagram and supporting document that outlines a scalable RAG-based AI system. Include components for document ingestion, vector database integration, LLM orchestration (e.g., LangChain/LlamaIndex), and secure API gateways, demonstrating how these integrate with existing enterprise systems like ERP or CRM.
Design an LLMOps and Governance Framework
Prepare a summary of your approach to managing the AI lifecycle in production. Focus on specific strategies for model registry, prompt versioning, observability (latency/cost/quality monitoring), and security controls such as prompt injection protection and PII masking.
Prepare a Case Study on AI Value Delivery
Draft a structured walkthrough of a past project where you translated a business requirement into a technical AI solution. Highlight the trade-offs made between buy vs. build approaches, the selection criteria for foundation models, and how you guided the delivery team from pilot to production.
Formulate Strategic Questions for the Interview
Prepare thoughtful questions regarding the organization's current data maturity, the specific business units prioritized for AI transformation, and how the Group IT team balances rapid innovation with the stringent governance requirements of the real estate and hospitality sectors.

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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