AI Architect (Group IT)
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- 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.