Lead Architect - Enterprise AI Data Platform

Hyphen Partners; Singapore; On-site; SGD 180,000 - 280,000; Full time

Job summary

Hyphen Partners collaborates with a global industrial AI software company to hire an experienced Enterprise Architect. You will own the data and knowledge foundation, turning IT, OT and sensor data into a governed semantic model for analytics, LLMs, and AI agents, while staying hands-on.

You will define the architecture, standards and roadmap for the data layer, build ingestion pipelines across SCADA, MES, OPC UA, MQTT, and enable RAG workflows with proper access control and auditability.

Qualifications

• 10+ years in large-scale enterprise software.
• Recent architect experience on distributed, service-based platforms.
• Hands-on knowledge graph and ontology experience (Neo4j, Neptune, RDF/OWL).
• Production experience architecting RAG, vector databases and agentic systems.
• Strong distributed systems and API platform fundamentals.
• Experience integrating IT data with OT/IoT data from industrial environments.
• Comfortable going deep into code, not just drawing boxes.

Responsibilities

• Set the architecture, standards and roadmap for the data and knowledge layer.
• Design the operational ontology and knowledge graph covering assets, sensors, events, locations and permitted actions.
• Architect ingestion and streaming pipelines across enterprise IT and industrial OT systems (SCADA, MES, historians, OPC UA, MQTT) across edge, hybrid and cloud.
• Build the retrieval layer feeding graph and vector context into RAG and agent workflows, with access control enforced end to end.
• Define how AI agents safely act on physical systems - guardrails, human approvals, auditability, fail-safe execution.
• Align product, engineering, security and compliance stakeholders across regions, and mentor engineers.

Skills

Enterprise software architecture

Distributed systems

Knowledge graphs/ontology

RAG/Vector databases

API platforms

IT/OT integration

Code proficiency

Tools

Neo4j

Neptune

RDF/OWL

Graph databases

OPC UA

MQTT

Job description

We're partnering with a global industrial AI software company whose platform runs across a large base of energy, building and industrial assets, serving hundreds of enterprise customers and recognised as a category leader by major industry analysts. They're moving beyond monitoring to AI that takes action in the physical world, and need an architect to build the data and knowledge foundation for it.

The role

You'll own the architecture that turns siloed IT, OT and sensor data into a governed, semantic model of real-world operations that analytics, LLMs and AI agents can rely on. Hands-on technical leadership: you set the direction and stay close enough to the code to keep it honest.

What you'll do

• Set the architecture, standards and roadmap for the data and knowledge layer
• Design the operational ontology and knowledge graph covering assets, sensors, events, locations and permitted actions
• Architect ingestion and streaming pipelines across enterprise IT and industrial OT systems (SCADA, MES, historians, OPC UA, MQTT) across edge, hybrid and cloud
• Build the retrieval layer feeding graph and vector context into RAG and agent workflows, with access control enforced end to end
• Define how AI agents safely act on physical systems - guardrails, human approvals, auditability, fail-safe execution
• Align product, engineering, security and compliance stakeholders across regions, and mentor engineers

What you'll bring

• 10+ years in large-scale enterprise software, with recent architect experience on distributed, service-based platforms
• Deep hands-on knowledge graph and ontology experience (e.g. Neo4j, Neptune, RDF/OWL or property graphs)
• Production experience architecting RAG, vector databases and agentic systems
• Strong distributed systems and API platform fundamentals
• Experience integrating IT data with OT/IoT data from industrial environments - this is the differentiator
• Comfortable going deep into code, not just drawing boxes