Engineering Manager - Security Ontology

Trend Micro•Austin, TX

About The Position

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.

Requirements

10+ years of software engineering experience, including significant experience building distributed systems, data-intensive applications, or large-scale platform services.
Demonstrated experience leading engineering teams comprising software engineers, data engineers, or both.
Proven track record of delivering complex, multi-team engineering initiatives from planning through production deployment and ongoing operations.
Strong understanding of distributed systems, cloud-native architectures, service-oriented architectures, and event-driven processing.
Experience designing and operating high-scale data ingestion, transformation, integration, and processing systems.
Strong background in data modeling, databases, APIs, distributed storage, and data-intensive services.
Experience with production programming languages such as Python, Java, Go, or equivalent.
Experience defining technical roadmaps, managing engineering execution, prioritizing competing investments, and delivering against measurable goals.
Ability to make sound architectural decisions, guide senior engineers, and balance technical depth with organizational and delivery responsibilities.
Strong communication and collaboration skills, with experience working across Product, Architecture, Data Platform, Security, and AI/ML organizations.
Strong understanding or practical experience in several of the following areas: Ontology engineering and domain modeling, Knowledge graphs and graph data modeling, Entity resolution, entity matching, and relationship discovery, Semantic models, semantic search, and knowledge representation, Schema mapping, data contracts, and ontology evolution, Data lineage, provenance, integrity, and governance, Graph-based retrieval and reasoning.
Experience with, or strong familiarity with, several of the following: LLM-powered applications and agent architectures, Retrieval-Augmented Generation (RAG) and hybrid retrieval, AI memory systems, embeddings, and vector databases, Agent tool integration and interoperability protocols such as MCP, Agent orchestration frameworks such as LangGraph, Temporal, or equivalent, Context construction, knowledge retrieval, and multi-agent collaboration, Production considerations for AI systems, including latency, cost, reliability, security, and evaluation.
The ability to understand the underlying architectural principles and guide engineering teams is more important than deep expertise in any single framework.

Nice To Haves

Experience with technologies such as Neo4j, Amazon Neptune, RDF/SPARQL, GraphQL, or equivalent graph technologies is highly desirable.
Experience working with security products, security data, or cybersecurity platforms in one or more of the following areas: Endpoint security / EDR, Identity security / IAM, Cloud security, Network security, SIEM and security analytics, Vulnerability management, Threat intelligence, Incident detection and response.
Understanding how entities and events connect across security domains is particularly valuable, for example: Identity → Device → Process → Network Connection → Threat → Incident
Experience integrating data across multiple security products or building a shared security data model is a strong advantage.

Responsibilities

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.
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.
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.
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.
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, storage architecture, governance, and self-service capabilities.
Ensure that the platform can accommodate evolving schemas, increasing data volumes, and new security domains without requiring extensive custom engineering for each integration.
Own the technical direction and engineering execution of the Ontology platform in alignment with the broader Security Platform and AI strategy.
Collaborate with platform architects and senior technical leaders to make sound architectural decisions and resolve complex technical trade-offs.
Convert architectural designs into actionable engineering plans, incremental releases, and production-ready implementations.
Lead design reviews, evaluate technology choices, and ensure consistency across services, data models, and integration patterns.
Establish standards for API design, distributed systems, data modeling, testing, CI/CD, security, and production operations.
Drive technical risk reduction through targeted prototypes, performance testing, capacity planning, and production validation.
Identify opportunities to simplify the architecture, improve developer productivity, reduce operational overhead, and accelerate delivery.
Communicate engineering progress, risks, dependencies, and delivery forecasts clearly to senior leadership and cross-functional stakeholders.