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About This Role
About the role:
At Rubrik IT, we are transforming how the enterprise thinks and operates — building a "thinking enterprise" where intelligent, agentic systems reason across our data, tools, and workflows to drive speed, quality, and measurable business impact. As an AI Architect, you will be a pivotal technical leader driving AI Native solutions that make this vision real.
You will operate where AI innovation, top-tier engineering, and business outcomes converge, acting as the technical bridge between complex business problems and cutting-edge AI execution. Leveraging our robust Enterprise AI Platform, you will translate ambiguous problems into concrete AI solution designs and ensure their successful deployment and measurable impact.
This role is designed for systems-thinkers and orchestrators with an AI-first mindset — leaders who move fluidly between hands-on building AI solutions, and who know how to harness model APIs, data sources, and workflow layers to build practical technology that serves the business.
What you'll do:
- Lead applied AI solution design and architecture, breaking down ambiguous business problems into concrete, actionable AI solution designs.
- Contribute to the detailed design of large-scale, distributed AI/ML systems, ensuring performance, reliability, and security.
- Design and improve retrieval, prompting, tool-calling, and orchestration patterns for internal use cases such as knowledge assistants, workflow automation, and decision support.
- Champion and enforce design standards, patterns, and best practices for scalable and secure development of AI applications across teams.
- Drive the hands-on development and implementation of key AI components, supporting both traditional and Generative AI model development and deployment.
- Build and deploy AI-enabled internal workflows that connect enterprise systems, data sources, model APIs, and automation layers.
- Leverage AI-assisted development to accelerate implementation, bringing a strong 'editor' mindset to ruthlessly audit, review, and secure AI-generated code against our standards for reliability, security, observability, and documentation.
- Lead the implementation and continuous improvement of MLOps pipelines, including automated model training, versioning, deployment, and monitoring.
- Develop and maintain evaluation approaches for output quality, retrieval accuracy, latency, and failure modes, and use findings to improve system performance over time.
- Help identify where human review, controls, and escalation paths are required to support responsible deployment of AI-enabled systems.
- Apply sound engineering judgment to balance speed, usability, risk, and maintainability in production and near-production environments.
- Proactively collaborate with executive leadership, data science, engineering, and product stakeholders to translate business use-cases into scalable AI solutions.
- Lead rigorous problem formulation by partnering with R&D,, Security, Data, HR, Finance, and business stakeholders — ensuring we apply AI to the right problems before translating needs into scalable technical solutions.
- Provide technical leadership and mentorship to other AI/ML engineers, fostering a culture of engineering excellence and hands-on experimentation.
- Contribute reusable components, playbooks, and patterns that reduce duplicate effort and improve speed across the engineering environment.
- Technical architecture and AI implementations directly to measurable business outcomes, recognizing that technology serves the business.
- Actively research and evaluate cutting-edge AI/ML techniques, algorithms, and models to identify opportunities for platform enhancement and new solution development.
Experience you'll need:
- 10+ years of core Software/Systems Engineering architecture experience, including 4+ years dedicated to building and deploying applied AI/ML systems in production.
- Bachelor’s degree in Computer Science, Engineering, or a related field (Master’s/Ph.D. is a plus).
- Direct hands-on experience with Generative AI technologies, including LLMs, Retrieval-Augmented Generation (RAG), context graphs, memory management, and agentic AI architectures.
- Strong programming skills in Python, Java, or Go. Deep proficiency with GenAI orchestration frameworks (e.g., LangGraph, LlamaIndex, AutoGen, Semantic Kernel) and a working knowledge of traditional ML frameworks (e.g., PyTorch, TensorFlow).
- Extensive hands-on experience with the modern AI data ecosystem, including Vector Databases (e.g., Pinecone, Weaviate) and Graph Databases (e.g., Neo4j).
- Experience with distributed systems architecture, event-driven streaming platforms, and cloud AI/ML platforms (e.g., GCP Vertex AI, AWS SageMaker, Azure OpenAI).
- Strong ability to work across data, model, application, and infrastructure layers, operating effectively with incomplete information and ambiguous problems.
- Experience integrating applications, data sources, or internal platforms through APIs, services, event-driven patterns, or workflow tools.
- Deep understanding of security principles as they apply to AI systems (e.g., prompt injection, data exfiltration, RBAC). Experience in enterprise-scale environments with meaningful privacy or compliance constraints.
- Experience with telemetry, tracing, and production monitoring for AI systems (e.g., LangSmith, Phoenix, Datadog).
- Experience contributing to open-source AI projects, publications, or active participation in the AI engineering community.
- Evidence of applied AI building — GitHub contributions, technical writing, demos, or open-source work demonstrating curiosity for emerging AI tooling.
- Prior experience in a solutions architecture, successfully bridging technical teams and senior business stakeholders.
The minimum and maximum base salaries for this role are posted below; additionally, the role is eligible for bonus potential, equity and benefits. The range displayed reflects the minimum and maximum target for new hire salaries for the role based on U.S. location. Within the range, the salary offered will be determined by work location and additional factors, including job-related skills, experience, and relevant education or training. US Pay Range $254,300—$356,300 USD Join Us in Securing and Accelerating the World's AI Transformation
Rubrik (RBRK), the Security and AI Operations Company, leads at the intersection of data protection, cyber resilience, and enterprise AI acceleration. Rubrik Security Cloud delivers complete cyber resilience by securing, monitoring, and recovering data, identities, and workloads across clouds. Rubrik Agent Cloud accelerates trusted AI agent deployments at scale by monitoring and auditing agentic actions, enforcing real-time guardrails, fine-tuning for accuracy and undoing agentic mistakes.
Inclusion @ Rubrik
At Rubrik, we are dedicated to fostering a culture where people from all backgrounds are valued, feel they belong, and believe they can succeed. Our commitment to inclusion is at the heart of our mission to secure the world’s data.
Our goal is to hire and promote the best talent, regardless of background. We continually review our hiring practices to ensure fairness and strive to create an environment where every employee has equal access to opportunities for growth and excellence. We believe in empowering everyone to bring their authentic selves to work and achieve their fullest potential.
Our inclusion strategy focuses on three core areas of our business and culture:
- Our Company: We are committed to building a merit-based organization that offers equal access to growth and success for all employees globally. Your potential is limitless here.
- Our Culture: We strive to create an inclusive atmosphere where individuals from all backgrounds feel a strong sense of belonging, can thrive, and do their best work. Your contributions help us innovate and break boundaries.
- Our Communities: We are dedicated to expanding our engagement with the communities we operate in, creating opportunities for underrepresented talent and driving greater innovation for our clients. Your impact extends beyond Rubrik, contributing to safer and stronger communities.
Equal Opportunity Employer/Veterans/Disabled
Rubrik is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.
Rubrik provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, Rubrik complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.
Federal law requires employers to provide reasonable accommodation to qualified individuals with disabilities. Please contact us at [email protected] if you require a reasonable accommodation to apply for a job or to perform your job. Examples of reasonable accommodation include making a change to the application process or work procedures, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.
Salary Context
This $254K-$356K range is above the 75th percentile for AI Architect roles in our dataset (median: $179K across 32 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,653 AI roles we're tracking, AI Architect positions make up 1% of the market. At Rubrik, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $230,000 based on 212 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($305K) sits 33% above the category median. Disclosed range: $254K to $356K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Rubrik AI Hiring
Rubrik has 2 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Positions span Palo Alto, CA, US, Palo Alto, CA. Compensation range: $356K - $356K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
Frequently Asked Questions
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