Data & Knowledge Intelligence Architect Lead

Univers; New Haven (CT); On-site; USD 150,000 - 210,000; Full time

Job summary

Univers in New Haven, CT seeks an Architect Lead - Data & Knowledge Intelligence to shape the enterprise platform's architecture, semantic design, and AI-readiness across IT/OT/IoT landscapes.

You will define target-state architecture for a unified, semantically rich foundation, lead Knowledge Graph and ontology strategy, and enable RAG, AI Agents, and secure, governed actions within physical systems.

Qualifications

• Proven experience designing and evolving service-oriented / microservice architectures with high availability and fault tolerance.
• Strong understanding of API architecture, authentication/authorization, rate limiting, versioning, and platform governance.
• Deep expertise in semantic modeling, knowledge graphs, ontology modeling and digital twin representations.
• Hands-on architectural experience with RAG pipelines, vector databases, LLM context provisioning, and AI-driven workflow execution.
• Experience with distributed systems fundamentals: consistency, concurrency, caching, messaging/streaming, multi-region resilience.

Responsibilities

• Define the technical roadmap, architectural standards, and system boundaries for the Data & Knowledge Intelligence layer.
• Architect an operational ontology representing assets, sensors, relationships, events, and governed actions.
• Establish standards using Knowledge Graphs and semantic approaches (RDF/OWL/SHACL, Property Graphs).
• Build capabilities for semantic mapping, master data management, asset hierarchy, and schema drift handling.
• Architect scalable ingestion, streaming, CDC pipelines across IT platforms, data lakes, and repositories.
• Lead integration strategies for OT systems (SCADA, MES, Historians) and IoT protocols (OPC UA, MQTT, Modbus).
• Design architectures resilient to connectivity issues and edge-to-cloud synchronization.
• Create a secure knowledge layer feeding LLMs, RAG, and AI Agents with governed actions.
• Ensure end-to-end data lineage, data quality, and access controls across graph and vector stores.

Skills

Microservice architecture

API design & governance

Knowledge Graphs & Ontology

AI & Agentic Systems

Distributed systems

Technical leadership

Data governance

Tools

GraphDB

Neo4j

Amazon Neptune

RDF/OWL/SHACL

Property Graphs

Job description

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

Home - Univers

Job Summary

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.

Key Responsibilities:

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 Jcapabilities 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.

Technical Leadership & Stakeholder Alignment:

• Establish clear API/SDK service contracts and reference architectures to ensure custom implementations strengthen the core product rather than creating fragmented one-off solutions.
• Lead design reviews and align stakeholders across product engineering, infrastructure, security, and compliance teams in multiple regions.
• Identify scalability and reliability bottlenecks; design long-term solutions for high-traffic, low-latency, globally distributed systems.
• Provide technical leadership and mentorship to engineers; raise the bar for engineering quality, documentation, and operational excellence.
• Evaluate new technologies and guide pragmatic adoption (e.g., platform frameworks, cloud patterns, distributed data systems).
• Drive cross-team execution for large initiatives: architecture roadmap, risk management, and delivery governance.
• Collaborate closely with product managers and business stakeholders to deeply understand business pain points, propose technical solutions, and drive mission-critical projects to completion.

Basic Qualifications:

• Proven experience designing and evolving service-oriented / microservice architectures, including high availability, fault tolerance, and performance optimization.
• Strong understanding of API architecture (REST/gRPC), authentication/authorization, rate limiting, versioning, developer experience, and platform governance.
• Knowledge Graphs & Ontology: Deep expertise in semantic modeling, ontology modeling, knowledge graph technologies (GraphDB, Neo4j, Amazon Neptune, RDF/OWL/SHACL, or Property Graphs), and digital twin representations.
• AI & Agentic Systems: Hands-on architectural experience with RAG pipelines, vector databases, LLM context provisioning, agentic tool permissioning, and AI-driven workflow execution.
• Deep knowledge of distributed systems fundamentals: consistency, concurrency, caching, messaging/streaming, and multi-region resilience.
• Hands-on ability to produce high-quality technical designs and influence implementation; comfortable diving into code when needed.
• Strong analytical and problem-solving skills, with a passion for engineering excellence and setting high technical standards in security architecture.
• 10+ years of experience in software design and development, with a track record of building large-scale enterprise systems.
• Solid understanding of distributed systems; hands-on experience in designing and developing platforms for traffic risk control, transaction fraud detection, or account security is highly preferred.
• Ability to thrive in a fast-paced, dynamic environment and drive projects forward with efficiency.
• Experience integrating heterogeneous enterprise systems and working with both IT data and OT/IoT data from industrial or physical environments.