Senior AI Product Architect

The Senior AI Product Architect is a senior technical leader within the Product organization responsible for defining the AI, data, and platform architecture that powers Anomali’s product strategy and for leading the technical execution of strategic AI-driven initiatives This role will help advance Anomali’s architecture thesis around the Intelligent Unification Layer, including the Governed Decisioning Layer, which provides the trusted data, context, governance, auditability, and control required for AI-driven security operations. The architecture supports Anomali’s two primary product experiences: Agentic SOC Platform Managed Intelligence as a Service — MIaaS The Senior AI Product Architect will ensure that technical decisions support Anomali’s five-level maturity model, enabling customers to progress from intelligence-enriched security operations through increasingly advanced levels of data unification, correlation, decisioning, automation, and agentic security operations Working closely with Product Management, Engineering, Data Science, UX, Product Marketing, and executive leadership, this role translates product doctrine and strategy into scalable technical architecture and executable implementation plans Engineering retains responsibility for people leadership, software delivery, and operational execution. The Senior AI Product Architect provides day-to-day technical leadership for engineers assigned to strategic initiatives, owns architectural integrity, and guides technical decisions across the product portfolio Define the technical architecture supporting Anomali’s Intelligent Unification Layer, Governed Decisioning Layer, Agentic SOC Platform, and MIaaS Translate product strategy, customer outcomes, and business requirements into scalable architecture and implementation plans Balance near-term delivery requirements with long-term scalability, maintainability, interoperability, and governance Ensure architecture decisions align with Anomali’s five-level maturity model and support customers at different stages of adoption Own the technical execution strategy for assigned product initiatives Ensure new capabilities align with Anomali’s long-term AI, data, intelligence, and platform vision Lead architecture reviews and approve technical designs for strategic product initiatives Establish architectural principles, engineering standards, and reusable platform patterns Ensure consistency across platform services, APIs, AI models, data services, shared services, and distributed infrastructure Partner closely with our Head of Field Product (International), Satya Roy, on field doctrine - customer adoption requirements, use cases, and the application of the five-level maturity model Partner closely with Senior Principal Product Manager, Patrick Holt, on product roadmap sequencing, platform evolution, and capability delivery Jointly evaluate architectural trade-offs, feasibility, sequencing, and dependencies with Product Management before commitments are made Clearly distinguish between: Capabilities available today Capabilities dependent on customer deployment posture or maturity level Future roadmap capabilities Ensure technical architecture remains aligned with approved product doctrine and customer-facing positioning Any unresolved disagreements between Architecture, Engineering, and Product Management will be escalated to the Head of Product and Engineering, who will make the final decision in consultation with executive leadership, as appropriate Define the long-term architecture for AI-driven and agentic security operations Design agent orchestration frameworks, reasoning pipelines, contextual decision systems, AI-assisted workflows, and human-in-the-loop controls Architect the Governed Decisioning Layer to support appropriate authorization, traceability, auditability, explainability, rollback, and policy enforcement Define architectural patterns that allow agents to operate against unified, normalized, deduplicated, and contextualized security data Ensure autonomous and semi-autonomous workflows operate within clearly defined risk, identity, permission, and governance boundaries Support the evolution from assisted investigation and decision support toward increasingly advanced agentic operations as product capabilities and customer readiness mature Evaluate emerging foundation models, agent frameworks, AI infrastructure, and security technologies for potential strategic adoption Architect large-scale enterprise data platforms supporting AI, analytics, operationalized intelligence, and cybersecurity workloads Define architecture for high-volume ingestion of telemetry, threat intelligence, identity, cloud, endpoint, network, and other security data Design scalable data normalization, enrichment, deduplication, correlation, storage, and retrieval services Ensure data entering the platform is governed, observable, attributable, and suitable for machine-speed analysis and decisioning Define trusted data foundations through governance, lineage, provenance, data quality, access control, and lifecycle management Architect petabyte-scale storage and processing patterns using modern distributed data technologies and open table formats where appropriate Optimize architecture for performance, resiliency, cost efficiency, sovereignty, and customer-controlled deployment requirements Architect solutions supporting cloud, regional VPC, sovereign cloud, on-premises, and hybrid deployment models as required by customer and product strategy Provide architectural direction for distributed search and low-latency retrieval across large security datasets Guide the use of vector databases, semantic search, hybrid search, embeddings, retrieval-augmented generation, and relevance optimization where appropriate Ensure AI systems have access to the operationalized intelligence, environmental context, identity context, and historical evidence required to produce trusted outcomes Partner with engineering specialists to optimize indexing, query performance, throughput, and retrieval quality Maintain sufficient technical depth to assess design quality and trade-offs without requiring the role to personally own every search or retrieval subsystem Partner with Data Science and AI Engineering teams to operationalize models and AI capabilities into scalable production systems Define platform architecture supporting inference, model lifecycle management, feature engineering, evaluation, observability, and continuous improvement Ensure AI capabilities are built on trusted, governed, and high-quality data Establish standards for model and agent evaluation, including accuracy, safety, traceability, resilience, and business outcomes Guide the integration of predictive, generative, and agentic capabilities into the broader product platform Lead the technical direction of cross-functional delivery teams comprising Product Managers, AI Engineers, Software Engineers, Data Engineers, UX, QA, DevCloudOps, and other specialists Provide day-to-day technical leadership throughout the software development lifecycle Work with Engineering Managers to align resources, dependencies, technical priorities, and delivery sequencing Remove architectural and technical blockers that threaten strategic initiatives Guide implementation decisions while preserving Engineering’s ownership of execution and operational delivery Ensure technical commitments are realistic, clearly scoped, and consistent with the approved roadmap Maintain architectural documentation, decision records, and clear technical accountability Participate in strategic customer engagements to understand technical requirements, deployment constraints, security posture, and desired business outcomes Represent Anomali’s architecture thesis with executive customers, strategic partners, analysts, and other external stakeholders Clearly explain the evolution from traditional SIEM and threat intelligence operating models toward AI-driven security operations powered by the Intelligent Unification Layer Communicate how Anomali can augment an existing security architecture across Levels 1–4 and support broader platform transformation at Level 5 when the customer is ready Avoid positioning roadmap capabilities as currently available and ensure all external technical discussions remain aligned with approved Product messaging Present technical strategy, architecture, trade-offs, and product direction to executive leadership Drive the architectural evolution of AI-native platform capabilities Champion reusable engineering services, common platform components, and architectural modernization Reduce technical debt through disciplined architecture planning and prioritization Continuously improve platform scalability, performance, resiliency, security, and operational efficiency Mentor architects, engineers, and technical leaders across the organization Promote clear technical decision-making, accountability, and documentation Benefits Prescription drug coverage Quarterly events with your geographic team Medical, dental, and vision insurance Short-term and long-term disability Life insurance (2x annual salary) Accidental death & dismemberment (AD&D) coverage Gym reimbursement Access to mental health apps and Employee Assistance Programs (EAP) Wellness initiatives including walking challenges 401(k) retirement plan with company matching and pension Competitive compensation packages Equity grants for eligible employees Medical and dependent care Flexible Spending Accounts (FSA) Medical and dependent care Flexible Spending Accounts (FSA) Paid holidays observed according to local country calendars Unlimited flexible time off (FTO) for U.S. employees Country-specific time off policies for international team members Office lunch and refreshments provided at headquarters Education and training Program Buddy Program for new hires Lunch and learn sessions across teams and topics Security data unificationIdentity, authorization, auditability, and policy enforcement for AI agentsStrong experience partnering with Product Management organizationsRetrieval-augmented generationDeep hands-on specialization in every area above is not requiredExperience making and documenting architectural trade-offs involving scope, timing, performance, scalability, security, and costData normalization, enrichment, governance, lineage, provenance, and qualityDemonstrated success defining and delivering enterprise-scale SaaS, cloud-native, data, or AI platformsStrong executive communication and presentation skillsMachine learning operations and model lifecycle managementLarge-scale data and platform architecture supporting AI and cybersecurity workloadsDistributed search and low-latency retrievalHuman-in-the-loop AIObservabilitySOARFeature engineering and inference pipelinesMinimum 8 years of experience, (12+ years of experience preferred) in software engineering, systems architecture, AI platform architecture, product architecture, or technical leadershipEmbeddings, hybrid retrieval, and relevance optimizationGoverned decisioningAPIs, microservices, orchestration, and shared platform servicesDeep expertise in large-scale data and distributed platform architectureEnterprise AI and agentic platform architectureVector databases and semantic searchCustomer maturity and adoption modelsCandidates should demonstrate strong technical depth in:Experience with one or more of the following:AI platform and agentic system architectureDeep expertise in enterprise AI or agentic platform architectureExperience translating product strategy into technical architecture, delivery sequencing, and implementation plansIdentity and non-human identitySecurity Information and Event ManagementThreat IntelligenceDeep specialization in every adjacent area is not requiredAbility to engage credibly with technical executives, architects, security leaders, and strategic customersAbility to understand and communicate:Technical product leadership, including translating product strategy into executable architectureData lake and lakehouse technologiesHigh-volume ingestion and streaming architecturesSecurity analyticsDistributed systems and cloud-native applicationsSIEM augmentation and modernizationXDRDemonstrated ability to lead engineers and technical teams through influence rather than direct reporting relationshipsAI-driven security operationsStrong understanding of enterprise cybersecurity platforms and operating modelsOperationalized intelligenceKubernetes and cloud-native infrastructurePetabyte-scale storage and data lifecycle managementExperience operating in environments requiring strong governance, sovereignty, auditability, and regulatory controlsCandidates should be able to evaluate architecture and guide specialists across:Large-scale data platforms supporting AI and analyticsExperience leading complex cross-functional initiatives from concept through production deliveryAgentic security operationsThe candidate should also be conversant in adjacent disciplines, including distributed search, retrieval-augmented generation, machine learning operations, streaming data infrastructure, and executive customer engagementOn-premises, sovereign, regional VPC, and customer-controlled deployment modelsEnterprise security architecture and security operationsHuman-in-the-loop and governed autonomous decision systemsExperience building AI-native enterprise software productsExperience designing platforms that support autonomous or semi-autonomous AI agentsExperience developing security analytics, threat intelligence, or security operations productsExperience with high-scale telemetry, security data, or observability platformsExperience working in a product-led organization where Product defines direction and priorities and Engineering owns execution and deliveryExperience presenting architecture and product strategy to CISOs, CIOs, CTOs, and enterprise architecture leaders #J-18808-Ljbffr Anomali