The Group IT Data Team is to establish scalable, governed, enterprise-grade AI capabilities to enhance operational efficiency, unlock business value, and accelerate digital transformation across FEO’s business units. The team works closely with business leaders, technology teams, cybersecurity, enterprise architecture, data owners, vendors, and partners to deliver trusted AI capabilities across the real estate value chain.
The AI Architect will be the enterprise design authority for AI architecture, responsible for defining AI reference architecture, platform standards, solution patterns, governance controls, and technical roadmaps. The role will focus on Generative AI, Large Language Models, AI agents, RAG, AI-ready data platforms, MLOps/LLMOps, and scalable integration of AI capabilities into enterprise systems.
The role is expected to translate business outcomes into practical AI solutions and guide delivery teams from concept through production.
Responsibilities
- Define enterprise AI reference architectures, AI platform standards, reusable solution patterns, and technical roadmaps aligned to FEO’s AI strategy, enterprise architecture, cybersecurity, data governance, and business priorities.
- Lead architecture design for GenAI, LLM, RAG, AI agents, multi-agent workflows, conversational AI, document intelligence, recommendation engines, predictive ML, computer vision, and NLP solutions.
- Evaluate and select suitable AI/ML technologies, foundation models, LLM providers, open-source models, orchestration frameworks, vector databases, cloud AI services, and AI infrastructure components based on use case, risk, cost, performance, data residency, and integration needs.
- Design AI-ready data foundations including document ingestion, chunking, embeddings, vector search, hybrid search, re-ranking, knowledge graphs, metadata schemas, data lineage, source attribution, and access-aware retrieval.
- Define and implement LLMOps / MLOps practices including model registry, prompt and pipeline versioning, evaluation frameworks, observability, monitoring of latency/quality/cost, model drift detection, retraining triggers, and deployment controls across DEV/UAT/PROD environments.
- Design AI-ready data foundations for RAG and agentic AI, including document ingestion, chunking strategies, embedding pipelines, vector databases, hybrid search, re-ranking, metadata schemas, knowledge graph design, source attribution, data lineage, freshness controls, and access-aware retrieval.
- Architect scalable AI platform environments across cloud, hybrid, and on-premises architectures, including secure APIs, microservices, containers, CI/CD pipelines, AI gateways, observability, and cost optimisation.
- Ensure secure integration with enterprise systems including ERP, CRM, data platforms, document management systems, IAM/SSO, workflow systems, and external APIs.
- Establish Responsible AI and AI governance controls, including human-in-the-loop design, explainability, bias mitigation, prompt injection protection, content safety, PII/confidential data handling, auditability, model risk controls, and compliance with internal policies and applicable Singapore regulatory expectations.
- Partner with business units to shape AI use cases, assess value and feasibility, define architecture options, recommend buy/build/partner approaches, and guide pilots through production scaling.
- Provide technical leadership to AI engineers, data engineers, solution architects, vendors, and delivery teams through architecture reviews, design documentation, technical standards, mentoring, and best-practice sharing.
- Maintain awareness of emerging AI technologies, agentic AI patterns, multimodal AI, model-context protocols, AI security risks, and enterprise AI platform trends; translate relevant developments into practical recommendations for FEO.
Requirements
- At least 8–12 years of experience in software engineering, data engineering, cloud architecture, enterprise architecture, or AI/ML-related roles; 12+ years preferred for Principal / Expert level.
- At least 3–5 years of hands-on experience designing, architecting, or delivering production AI/ML, GenAI, LLM, or data/AI platform solutions.
- Strong hands-on knowledge of GenAI/LLM technologies such as Azure OpenAI, OpenAI-compatible APIs, AWS Bedrock, Google Vertex AI, SAP AI capabilities, or equivalent enterprise AI platforms.
- Strong understanding of RAG architecture, including document ingestion, chunking, embeddings, vector databases, hybrid search, re-ranking, prompt design, source attribution, and evaluation.
- Experience with AI orchestration and agent frameworks such as Semantic Kernel, LangChain, LlamaIndex, LangGraph, AutoGen, CrewAI, or equivalent.
- Strong understanding of MLOps / LLMOps including CI/CD, model registry, deployment automation, monitoring, observability, drift detection, evaluation, and model/prompt lifecycle management.
- Strong cloud and platform architecture experience, preferably Azure, with working understanding of one or more of AWS or GCP.
- Proficiency in Python and SQL; experience with APIs, microservices, data pipelines, and integration patterns.
- Experience with containers and deployment technologies such as Docker, Kubernetes, GitHub Actions, Azure DevOps, or equivalent.
- Strong understanding of enterprise data architecture, including data lakes/lakehouses, warehouses, ETL/ELT, metadata, data quality, data lineage, access control, and governance.
- Strong understanding of AI security and governance, including privacy, identity, audit logging, role-based access, prompt injection risks, hallucination controls, model risk, and responsible AI principles.
- Ability to translate business needs into architecture options, business cases, implementation roadmaps, and executive-ready recommendations.
- Experience with SAP ecosystem, Microsoft stack, enterprise applications, or real estate / hospitality / retail / property operations systems is an advantage.
- Degree in Computer Science, Information Technology, Engineering, Data Science, AI, or equivalent; Master’s degree or relevant architecture/cloud/AI certifications are preferred.
- Excellent communication, stakeholder management, documentation, and technical leadership skills, with the ability to guide cross-functional teams and mentor engineers.