Data & Knowledge Intelligence – Architect Lead

ABOUT UNIVERS

Univers combines global operating scale, deep industrial intelligence, and recognition from the institutions shaping energy, technology, and sustainability. The platforms built for the last decade were designed to monitor.

Univers was built to act — not just report.

Univers operates at global scale:

• 1,070 GW+ Energy assets under AI management • 450M+ Connected sensors and devices • 800+ Enterprise customers • Leader Gartner Magic Quadrant Leader

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ROLE OVERVIEW / THE OPPORTUNITY

We are seeking a visionary and hands-on Architect Lead - Data & Knowledge Intelligence to lead the technical architecture, semantic design, and AI-readiness for our enterprise Physical AI platform. In this role, you will define and execute the target-state architecture that bridges fragmented IT, OT and IoT (sensor telemetry) environments, transforming raw heterogeneous data into a unified, semantically rich operational foundation.

You will serve as the principal authority on Knowledge Graphs, Industrial Ontologies, Semantic Search, and Contextual AI Services. You will architect a permission-aware knowledge layer that enables advanced analytics, Retrieval-Augmented Generation (RAG), and autonomous AI Agents to reason, predict, and execute safe, governed actions back into operational physical systems.

RESPONSIBILITIES / WHAT YOU WILL DO

Data Foundation & Knowledge Intelligence Architecture

• Define the technical roadmap, architectural standards, and system boundaries for the Data & Knowledge Intelligence layer. • Architect an operational domain ontology representing real-world physical assets, sensors, spatial-temporal relationships, events, business logic, and governed operational actions. • Establish standards using Knowledge Graphs and semantic approaches (e.g., RDF, OWL, SHACL, or Property Graphs) where they deliver concrete enterprise value. • Build core capabilities for semantic mapping, master & reference data management (MDM), asset hierarchy management, schema drift handling, and temporal/geospatial entity resolution. • Architect scalable ingestion, streaming, and Change Data Capture (CDC) pipelines connecting enterprise IT platforms, relational databases, data lakes, and document repositories. • Lead integration strategies for industrial OT systems (SCADA, MES, Historians, BMS/EMS) and IoT protocols (OPC UA, MQTT, Modbus) across edge, hybrid, and cloud environments. • Design architectures resilient to intermittent network connectivity, out-of-order time-series telemetry, schema evolution, and edge-to-cloud synchronization. • Create a secure, permission-aware context and semantic retrieval layer that feeds vectorized, graph-backed operational context into LLMs, RAG, and AI Agent workflows. • Architect mechanisms for AI Agents to execute write-backs or control actions into physical operational systems, enforcing strict policy controls, human-in-the-loop approvals, auditability, idempotency, and fail-safe handling. • Ensure end-to-end data lineage, data quality, tenant isolation, and fine-grained access control across all graph and vector knowledge stores.