AI Architect (Group IT)

Overview

The Group IT Data Team at Far East Organization seeks to establish scalable, governed, enterprise-grade artificial intelligence capabilities to boost operational efficiency, unlock business value, and accelerate digital transformation across its business units. Working alongside business leaders, technology teams, cybersecurity experts, enterprise architects, data owners, vendors, and partners, the team aims to deliver trusted AI solutions throughout the real estate value chain.

The AI Architect will serve as the enterprise design authority for AI architecture, defining AI reference frameworks, platform standards, solution patterns, governance controls, and technical roadmaps. Focus areas include Generative AI, Large Language Models (LLM), AI agents, Retrieval-Augmented Generation (RAG), AI-enabled data platforms, MLOps/LLMOps, and seamless integration of AI solutions into enterprise systems.

Key Responsibilities

• Define enterprise AI reference architectures, platform standards, reusable solution patterns, and technical roadmaps that align with the organization's AI strategy, enterprise architecture, cybersecurity guidelines, data governance policies, and business goals.
• Lead design efforts for systems involving Generative AI, LLMs, RAG, AI agents, multi-agent workflows, conversational AI, document intelligence, recommendation engines, predictive machine learning, computer vision, and natural language processing.
• Assess and select appropriate AI/ML technologies including foundation models, LLM providers, open-source models, orchestration frameworks, vector databases, cloud AI services, and infrastructural components by analyzing use cases, risk factors, cost, performance metrics, data residency, and integration requirements.
• Architect AI-ready data foundations comprising document ingestion, chunking, embedding pipelines, vector search, hybrid and re-ranking searches, knowledge graphs, metadata schemas, data lineage, source attribution, and access-aware retrieval mechanisms.
• Establish and implement MLOps/LLMOps practices including model registries, prompt and pipeline versioning, evaluation frameworks, latency/quality/cost monitoring, model drift detection, retraining triggers, and deployment governance across development, testing, and production environments.
• Design scalable AI platform environments accommodating cloud, hybrid, and on-premises infrastructures, incorporating secure APIs, microservices, containerization, continuous integration/continuous deployment (CI/CD) pipelines, AI gateways, observability tools, and cost optimization.
• Ensure secure and reliable integration with enterprise systems such as ERP, CRM, data platforms, document management, identity and access management (IAM/SSO), workflow systems, and third-party APIs.
• Implement Responsible AI and governance frameworks including human-in-the-loop mechanisms, explainability, bias mitigation, prompt injection protection, content safety protocols, PII/confidential data handling, auditability, model risk management, and compliance with organizational policies and applicable Singapore regulations.
• Collaborate with business units to identify AI use cases, evaluate their value and feasibility, define architectural options, suggest build/buy/partner strategies, and support pilot projects through to production scale.
• Provide technical leadership to AI engineers, data engineers, solution architects, vendors, and delivery teams via architecture reviews, design documentation, standards creation, mentoring, and dissemination of best practices.
• Stay current with emerging AI technologies including agentic AI patterns, multimodal AI, model-context protocols, AI security challenges, and trends in enterprise AI platforms, and translate relevant advances into actionable recommendations for the organization.

Candidate Requirements

• Possess 8 to 12 years of experience in software engineering, data engineering, cloud architecture, enterprise architecture, or AI/ML-related disciplines; candidates with over 12 years are preferred for senior or principal-level roles.
• Carry 3 to 5 years of direct experience in designing, architecting, or deploying production AI/ML solutions, Generative AI, LLMs, or data/AI platforms.
• Demonstrate strong practical knowledge of Generative AI/LLM technologies such as Azure OpenAI, OpenAI-compatible APIs, AWS Bedrock, Google Vertex AI, SAP AI capabilities, or similar enterprise AI platforms.
• Understand Retrieval-Augmented Generation (RAG) architecture thoroughly, including document ingestion, chunking strategies, embeddings, vector databases, hybrid search methods, re-ranking, prompt engineering, source attribution, and evaluation techniques.
• Experience with AI orchestration and agent frameworks like Semantic Kernel, LangChain, LlamaIndex, LangGraph, AutoGen, CrewAI, or their equivalents.
• Well-versed in MLOps and LLMOps practices including CI/CD pipelines, model registries, deployment automation, monitoring and observability, detection of model drift, evaluation processes, and lifecycle management of models and prompts.
• Have a strong background in cloud and platform architecture—preferably Azure—while also familiar with AWS or Google Cloud Platform.
• Proficient in programming languages such as Python and SQL, and experienced with APIs, microservices architecture, data pipeline implementation, and integration patterns.
• Skilled in containerization and deployment tools such as Docker, Kubernetes, GitHub Actions, Azure DevOps, or equivalent technologies.
• Hold solid knowledge of enterprise data architecture including data lakes, lakehouses, data warehouses, ETL/ELT processes, metadata management, data quality, lineage tracking, access controls, and governance frameworks.
• Aware of AI security and governance considerations including data privacy, identity management, audit logging, role-based access control, prompt injection risks, hallucination mitigation, model risk, and ethical AI principles.
• Able to translate business objectives into concrete architecture alternatives, business cases, implementation plans, and executive-level recommendations.
• Experience with the SAP ecosystem, Microsoft technology stack, enterprise applications, or domains such as real estate, hospitality, retail, or property operations is beneficial.
• Possess a degree in Computer Science, Information Technology, Engineering, Data Science, AI, or related fields; a Master’s degree or professional certifications in architecture, cloud computing, or AI are advantageous.
• Exhibit outstanding communication abilities, stakeholder management skills, meticulous documentation practices, and technical leadership qualities enabling effective guidance of cross-functional teams and mentorship of engineers.