Senior AI Product Architect (US Remote)

Company Description:
Anomali, headquartered in Silicon Valley, delivers the first Intelligence-Native Agentic SOC Platform — unifying a security data lake, the world's largest IOC repository, threat intelligence, and agentic AI into a single modern experience. The platform accelerates detection, investigation, and response, delivering earlier insights, faster action, and scalable modernization across any environment.
Whether augmenting existing tools or delivering complete SOC capabilities end-to-end, Anomali empowers security teams to operate faster, smarter, and with confidence.
Beyond Detecting. Start Deciding. Start Acting.
Learn more at www.anomali.com
Job Description:
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.
Core Areas of Expertise:
The successful candidate brings deep expertise in the following areas:
Enterprise AI and agentic platform architecture
Large-scale data and platform architecture supporting AI and cybersecurity workloads
Technical product leadership, including translating product strategy into executable architecture
The candidate should also be conversant in adjacent disciplines, including distributed search, retrieval-augmented generation, machine learning operations, streaming data infrastructure, and executive customer engagement. Deep specialization in every adjacent area is not required.
Key Responsibilities:
Product and Architecture Leadership
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
Product Management Partnership
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
AI and Agentic Architecture
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
Data and Platform Architecture
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
Search, Retrieval, and Context Engineering
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
AI Engineering and Data Science Partnership
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
Cross-Functional Delivery
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 w

Work arrangement
Yes