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About This Role
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 with the approved roadmap.
- Maintain architectural documentation, decision records, and clear technical accountability.
Customer and Executive Engagement
- 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.
Platform Innovation and Technical Excellence
- 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.
Security \& Privacy Responsibilities
- This role includes responsibilities related to the security and privacy of Anomali's information systems and data across corporate and cloud environments. Access to systems and data is granted based on role requirements, and individuals are expected to comply with Anomali security and privacy policies, complete required training, and safeguard sensitive company and customer information in accordance with the applicable security standards and regulatory requirements.
Qualifications
Required Skills/Experience:
- Minimum 8 years of experience, (12\+ years of experience preferred) in software engineering, systems architecture, AI platform architecture, product architecture, or technical leadership.
- Demonstrated success defining and delivering enterprise\-scale SaaS, cloud\-native, data, or AI platforms.
- Deep expertise in enterprise AI or agentic platform architecture.
- Deep expertise in large\-scale data and distributed platform architecture.
- Experience translating product strategy into technical architecture, delivery sequencing, and implementation plans.
- Experience leading complex cross\-functional initiatives from concept through production delivery.
- Demonstrated ability to lead engineers and technical teams through influence rather than direct reporting relationships.
- Strong experience partnering with Product Management organizations.
- Experience making and documenting architectural trade\-offs involving scope, timing, performance, scalability, security, and cost.
- Strong executive communication and presentation skills.
- Ability to engage credibly with technical executives, architects, security leaders, and strategic customers.
- This position is not eligible for employment visa sponsorship. The successful candidate must not now, or in the future, require visa sponsorship to work in the US.
- For candidates in the Bay area (preferred), this position is onsite/hybrid at our Redwood City, CA HQ. Currently, the team is working a hybrid schedule: Mon/Tue/Wed onsite and Thu/Fri remote. We will also consider remote candidates located within the United States.
Required Technical Depth:
Candidates should demonstrate strong technical depth in:
- AI platform and agentic system architecture
- Distributed systems and cloud\-native applications
- APIs, microservices, orchestration, and shared platform services
- Large\-scale data platforms supporting AI and analytics
- Data normalization, enrichment, governance, lineage, provenance, and quality
- High\-volume ingestion and streaming architectures
- Enterprise security architecture and security operations
- Human\-in\-the\-loop and governed autonomous decision systems
- Identity, authorization, auditability, and policy enforcement for AI agents
Conversant Knowledge:
Candidates should be able to evaluate architecture and guide specialists across:
- Data lake and lakehouse technologies
- Petabyte\-scale storage and data lifecycle management
- Distributed search and low\-latency retrieval
- Vector databases and semantic search
- Retrieval\-augmented generation
- Embeddings, hybrid retrieval, and relevance optimization
- Machine learning operations and model lifecycle management
- Feature engineering and inference pipelines
- Kubernetes and cloud\-native infrastructure
- On\-premises, sovereign, regional VPC, and customer\-controlled deployment models
Deep hands\-on specialization in every area above is not required.
Cybersecurity and Product Knowledge:
- Strong understanding of enterprise cybersecurity platforms and operating models.
- Experience with one or more of the following:
- + Security Information and Event Management
+ Threat Intelligence
+ Security analytics
+ XDR
+ SOAR
+ Identity and non\-human identity
+ Observability
+ AI\-driven security operations
- Ability to understand and communicate:
- + Operationalized intelligence
+ Governed decisioning
+ Human\-in\-the\-loop AI
+ Customer maturity and adoption models
+ Security data unification
+ SIEM augmentation and modernization
+ Agentic security operations
Preferred Qualifications:
- Experience building AI\-native enterprise software products.
- Experience designing platforms that support autonomous or semi\-autonomous AI agents.
- Experience developing security analytics, threat intelligence, or security operations products.
- Experience with high\-scale telemetry, security data, or observability platforms.
- Experience presenting architecture and product strategy to CISOs, CIOs, CTOs, and enterprise architecture leaders.
- Experience working in a product\-led organization where Product defines direction and priorities and Engineering owns execution and delivery.
- Experience operating in environments requiring strong governance, sovereignty, auditability, and regulatory controls.
Equal Opportunities Monitoring
*It is our policy to ensure that all eligible persons have equal opportunity for employment and advancement on the basis of their ability, qualifications and aptitude. We select those suitable for appointment solely on the basis of merit without regard to an individual's disability, race, color, religion, sex, sexual orientation, gender identity, national origin, age, or status as a protected veteran. Monitoring is carried out to ensure that our equal opportunity policy is effectively implemented.*
*If you are interested in applying for employment with Anomali and need special assistance or accommodation to apply for a posted position, contact our Recruiting team at* *[email protected]**.*
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Anomali, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Anomali AI Hiring
Anomali has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Redwood City, CA, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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