Engineering Manager - Security Ontology

TrendAI™, the global AI security leader and enterprise business unit of Trend Micro, empowers organizations with full AI visibility and consolidated security that inspires confidence, drives innovation, and eliminates risk.
At TrendAI™, we’re always seeking exceptional talent; people who want to collaborate with the best and push boundaries together. Here, your work goes beyond building a career. You will help protect what matters and play a vital role in shaping a safer, more trustworthy AI-powered future.
AI Fearlessly.
In this role, you will lead a team of data engineers and software engineers responsible for building, scaling, and operating the foundational knowledge systems that enable AI agents to understand security context, reason across products, maintain persistent knowledge, and power intelligent security experiences.
You will own the execution of the Ontology engineering roadmap, translating platform strategy and architectural direction into production-grade capabilities. You will work closely with Product, Architecture, Data Platform, Security Research, and AI/ML teams to establish shared semantic models, integrate data across security products, and deliver scalable services that enable agentic security workflows.
This is a technical leadership and delivery role requiring strong engineering skills, the ability to lead complex cross-team initiatives, and a track record of building high-performing engineering teams. You will guide architectural decisions, establish engineering standards, manage execution and dependencies, and ensure the platform delivers measurable business value at scale.
Key Responsibilities
Engineering Leadership & Delivery
Lead, grow, and develop a team of data engineers and software engineers responsible for the Security Ontology platform.
Own the end-to-end delivery of the Ontology engineering roadmap, from technical planning and prioritization through implementation, production rollout, and ongoing operations.
Translate product and platform strategy into clear engineering objectives, milestones, execution plans, and measurable outcomes.
Establish effective engineering processes, development standards, code quality practices, and delivery metrics.
Drive execution across multiple workstreams, proactively managing dependencies, technical risks, resource allocation, and delivery commitments.
Build a culture of ownership, accountability, technical excellence, and continuous improvement.
Mentor engineering leads and senior engineers, develop technical talent, and build a scalable organization capable of supporting the platform's long-term growth.
Balance near-term product delivery with foundational investments in architecture, reliability, performance, and developer productivity.
Security Ontology & Semantic Modeling
Lead the development of a shared security ontology spanning endpoint, identity, cloud, network, email, threat intelligence, and other security domains.
Establish canonical entities, relationships, attributes, semantic contracts, and domain modeling standards across security products.
Drive ontology versioning, evolution, governance, and interoperability practices that support independent product development without fragmenting the shared knowledge model.
Lead the definition and implementation of reusable patterns for mapping product-specific schemas into the common semantic model.
Deliver capabilities for entity resolution, relationship discovery, provenance, confidence scoring, and cross-product correlation.
Partner with product engineering teams to onboard new security domains and evolve existing integrations as product schemas and capabilities change.
Knowledge Graph & Ontology Services
Lead the design, implementation, and scaling of the Security Knowledge Graph and its supporting services.
Deliver a production-grade Ontology API and services layer that provides consistent semantic access to security knowledge for applications and AI agents.
Drive the implementation of entity resolution, context retrieval, graph traversal, semantic search, knowledge enrichment, and reasoning capabilities.
Define service abstractions that decouple applications and agents from underlying storage technologies and implementation details.
Evaluate and guide the adoption of graph, vector, relational, and hybrid storage technologies based on scale, performance, reliability, and operational requirements.
Establish service-level objectives and engineering practices for availability, latency, throughput, data consistency, and operational observability.
Continuously improve platform scalability and cost efficiency as data volumes, product integrations, and agent workloads grow.
AI Memory & Agentic Knowledge Integration
Lead the engineering work required to integrate the Security Ontology and Knowledge Graph with AI agent memory and orchestration systems.
Establish clear boundaries between shared ontology knowledge, persistent knowledge graph data, agent memory, vector stores, and traditional application data.
Deliver reusable capabilities for context retrieval, memory persistence, consolidation, expiration, relevance, and provenance.
Enable AI agents to leverage knowledge from previous investigations while preserving data integrity, access controls, and appropriate isolation.
Define and implement secure patterns for agents to access, enrich, and contribute knowledge through APIs, tools, and protocols such as MCP.
Partner with AI/ML and agent-platform teams to enable shared knowledge, multi-agent collaboration, and scalable autonomous security workflows.
Ensure that knowledge retrieval and memory capabilities meet production requirements for performance, reliability, security, and cost.
Data Engineering & Product Integration
Lead the development of scalable data ingestion, transformation, mapping, and knowledge-publishing pipelines across product databases, APIs, event streams, and existing data platforms.
Establish reusable integration frameworks and standards for schema mapping, data contracts, entity resolution, and semantic data publishing.
Drive onboarding of security products into the shared ontology, reducing integration effort and time to deliver new product capabilities.
Establish requirements for data lineage, provenance, freshness, integrity, quality, and observability across the knowledge lifecycle.
Partner with the Data Platform organization to align on data ownership, access patterns, sto