AI Architect (Group IT) at Far East Organization – Singapore | Jobyssey

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.

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.

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