{"id":2395095,"url":"https://alion.io/job/trend-micro-engineering-manager-security-ontology","title":"Engineering Manager - Security Ontology","company":{"id":20752,"name":"Trend Micro","domain":"trendmicro.com","url":"https://alion.io/company/trend-micro","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":76,"open_postings":44,"ghost_share":0,"stale_share":0.955,"repost_share":0,"time_to_fill_p50_days":48,"computed_at":"2026-10-13T05:45:00Z"}},"role":"Leadership","role_family":"Leadership","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Austin, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":179000,"max_usd":362000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1099},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon Neptune","optional":false},{"name":"CI/CD","optional":false},{"name":"Go","optional":false},{"name":"GraphQL","optional":false},{"name":"Java","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Neo4j","optional":false},{"name":"Python","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Trend Micro","optional":false},{"name":"Embeddings","optional":true},{"name":"IAM","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LLM","optional":true},{"name":"RAG","optional":true},{"name":"SIEM","optional":true}],"status":"live","first_seen_at":"2026-10-12T22:22:58Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-13T17:45:20Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"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.\nAt 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.\nAI Fearlessly.\nIn 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.\nYou 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.\nThis 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.\nKey Responsibilities\nEngineering Leadership & Delivery\nLead, grow, and develop a team of data engineers and software engineers responsible for the Security Ontology platform.\nOwn the end-to-end delivery of the Ontology engineering roadmap, from technical planning and prioritization through implementation, production rollout, and ongoing operations.\nTranslate product and platform strategy into clear engineering objectives, milestones, execution plans, and measurable outcomes.\nEstablish effective engineering processes, development standards, code quality practices, and delivery metrics.\nDrive execution across multiple workstreams, proactively managing dependencies, technical risks, resource allocation, and delivery commitments.\nBuild a culture of ownership, accountability, technical excellence, and continuous improvement.\nMentor engineering leads and senior engineers, develop technical talent, and build a scalable organization capable of supporting the platform's long-term growth.\nBalance near-term product delivery with foundational investments in architecture, reliability, performance, and developer productivity.\nSecurity Ontology & Semantic Modeling\nLead the development of a shared security ontology spanning endpoint, identity, cloud, network, email, threat intelligence, and other security domains.\nEstablish canonical entities, relationships, attributes, semantic contracts, and domain modeling standards across security products.\nDrive ontology versioning, evolution, governance, and interoperability practices that support independent product development without fragmenting the shared knowledge model.\nLead the definition and implementation of reusable patterns for mapping product-specific schemas into the common semantic model.\nDeliver capabilities for entity resolution, relationship discovery, provenance, confidence scoring, and cross-product correlation.\nPartner with product engineering teams to onboard new security domains and evolve existing integrations as product schemas and capabilities change.\nKnowledge Graph & Ontology Services\nLead the design, implementation, and scaling of the Security Knowledge Graph and its supporting services.\nDeliver a production-grade Ontology API and services layer that provides consistent semantic access to security knowledge for applications and AI agents.\nDrive the implementation of entity resolution, context retrieval, graph traversal, semantic search, knowledge enrichment, and reasoning capabilities.\nDefine service abstractions that decouple applications and agents from underlying storage technologies and implementation details.\nEvaluate and guide the adoption of graph, vector, relational, and hybrid storage technologies based on scale, performance, reliability, and operational requirements.\nEstablish service-level objectives and engineering practices for availability, latency, throughput, data consistency, and operational observability.\nContinuously improve platform scalability and cost efficiency as data volumes, product integrations, and agent workloads grow.\nAI Memory & Agentic Knowledge Integration\nLead the engineering work required to integrate the Security Ontology and Knowledge Graph with AI agent memory and orchestration systems.\nEstablish clear boundaries between shared ontology knowledge, persistent knowledge graph data, agent memory, vector stores, and traditional application data.\nDeliver reusable capabilities for context retrieval, memory persistence, consolidation, expiration, relevance, and provenance.\nEnable AI agents to leverage knowledge from previous investigations while preserving data integrity, access controls, and appropriate isolation.\nDefine and implement secure patterns for agents to access, enrich, and contribute knowledge through APIs, tools, and protocols such as MCP.\nPartner with AI/ML and agent-platform teams to enable shared knowledge, multi-agent collaboration, and scalable autonomous security workflows.\nEnsure that knowledge retrieval and memory capabilities meet production requirements for performance, reliability, security, and cost.\nData Engineering & Product Integration\nLead the development of scalable data ingestion, transformation, mapping, and knowledge-publishing pipelines across product databases, APIs, event streams, and existing data platforms.\nEstablish reusable integration frameworks and standards for schema mapping, data contracts, entity resolution, and semantic data publishing.\nDrive onboarding of security products into the shared ontology, reducing integration effort and time to deliver new product capabilities.\nEstablish requirements for data lineage, provenance, freshness, integrity, quality, and observability across the knowledge lifecycle.\nPartner with the Data Platform organization to align on data ownership, access patterns, storage architecture, governance, and self-service capabilities.\nEnsure that the platform can accommodate evolving schemas, increasing data volumes, and new security domains without requiring extensive custom engineering for each integration.\nTechnical Strategy, Architecture & Execution\nOwn the technical direction and engineering execution of the Ontology platform in alignment with the broader Security Platform and AI strategy.\nCollaborate with platform architects and senior technical leaders to make sound architectural decisions and resolve complex technical trade-offs.\nConvert architectural designs into actionable engineering plans, incremental releases, and production-ready implementations.\nLead design reviews, evaluate technology choices, and ensure consistency across services, data models, and integration patterns.\nEstablish standards for API design, distributed systems, data modeling, testing, CI/CD, security, and production operations.\nDrive technical risk reduction through targeted prototypes, performance testing, capacity planning, and production validation.\nIdentify opportunities to simplify the architecture, improve developer productivity, reduce operational overhead, and accelerate delivery.\nCommunicate engineering progress, risks, dependencies, and delivery forecasts clearly to senior leadership and cross-functional stakeholders.\nTechnical Skills & Experience\nRequired\n10+ years of software engineering experience, including significant experience building distributed systems, data-intensive applications, or large-scale platform services.\nDemonstrated experience leading engineering teams comprising software engineers, data engineers, or both.\nProven track record of delivering complex, multi-team engineering initiatives from planning through production deployment and ongoing operations.\nStrong understanding of distributed systems, cloud-native architectures, service-oriented architectures, and event-driven processing.\nExperience designing and operating high-scale data ingestion, transformation, integration, and processing systems.\nStrong background in data modeling, databases, APIs, distributed storage, and data-intensive services.\nExperience with production programming languages such as Python, Java, Go, or equivalent.\nExperience defining technical roadmaps, managing engineering execution, prioritizing competing investments, and delivering against measurable goals.\nAbility to make sound architectural decisions, guide senior engineers, and balance technical depth with organizational and delivery responsibilities.\nStrong communication and collaboration skills, with experience working across Product, Architecture, Data Platform, Security, and AI/ML organizations.\nOntology & Knowledge Graph\nStrong understanding or practical experience in several of the following areas:\nOntology engineering and domain modeling.\nKnowledge graphs and graph data modeling.\nEntity resolution, entity matching, and relationship discovery.\nSemantic models, semantic search, and knowledge representation.\nSchema mapping, data contracts, and ontology evolution.\nData lineage, provenance, integrity, and governance.\nGraph-based retrieval and reasoning.\nExperience with technologies such as Neo4j, Amazon Neptune, RDF/SPARQL, GraphQL, or equivalent graph technologies is highly desirable.\nAI & Agentic Systems\nExperience with, or strong familiarity with, several of the following:\nLLM-powered applications and agent architectures.\nRetrieval-Augmented Generation (RAG) and hybrid retrieval.\nAI memory systems, embeddings, and vector databases.\nAgent tool integration and interoperability protocols such as MCP.\nAgent orchestration frameworks such as LangGraph, Temporal, or equivalent.\nContext construction, knowledge retrieval, and multi-agent collaboration.\nProduction considerations for AI systems, including latency, cost, reliability, security, and evaluation.\nThe ability to understand the underlying architectural principles and guide engineering teams is more important than deep expertise in any single framework.\nPreferred: Cybersecurity Experience\nExperience working with security products, security data, or cybersecurity platforms in one or more of the following areas:\nEndpoint security / EDR.\nIdentity security / IAM.\nCloud security.\nNetwork security.\nSIEM and security analytics.\nVulnerability management.\nThreat intelligence.\nIncident detection and response.\nUnderstanding how entities and events connect across security domains is particularly valuable, for example:\nIdentity → Device → Process → Network Connection → Threat → Incident\nExperience integrating data across multiple security products or building a shared security data model is a strong advantage.\nWhat Success Looks Like\nIn the first 12-18 months, you will lead the team in delivering the following outcomes:\nA production-ready Security Ontology: A shared, governed semantic model connecting entities, relationships, and security context across multiple Vision One products.\nA scalable Knowledge Graph: A reliable, high-performance foundation for representing and querying security relationships across growing data volumes and domains.\nA production-grade Ontology API and services layer: Consistent, secure, and observable semantic access for applications, platform services, and AI agents.\nRepeatable product onboarding: Standardized ingestion, schema mapping, entity resolution, and knowledge-publishing workflows that reduce the time and effort required to integrate new products.\nCross-product entity resolution: Reliable identification and corre...","description_format":"text","description_chars":14524,"description_truncated":true,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity","Life insurance","Vision 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