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About This Role
Enterprise AI Architect
Caltech
Job Category: Fulltime Regular
Exempt Overtime Eligible: Exempt
Benefits Eligible: Benefit Based
Caltech is a world-renowned science and engineering institute that marshals some of the world's brightest minds and most innovative tools to address fundamental scientific questions. We thrive on finding and cultivating talented people who are passionate about what they do. Join us and be a part of the diverse Caltech community.
Job Summary
The Enterprise AI Architect is responsible for designing, building, deploying, and operating enterprise-grade AI solutions that support the Institute's administrative, research, educational, and student service functions. This role serves as the Institute's senior technical expert in Generative AI, responsible for translating emerging AI capabilities into secure, scalable, and production-ready enterprise applications.
The successful candidate will bring deep expertise in large language models (LLMs), agentic architectures, retrieval-augmented generation (RAG), workflow orchestration and enterprise integrations, in addition to modern software engineering practices. This is a highly technical role focused on hands-on solution architecture, rapid prototyping, production engineering, and technical leadership across the AI technology stack.
This is an Organizational Critical position. In the event of an emergency on campus, an employee designated as organizational critical is expected to report to Campus as soon as possible to assist in division/department response and recovery efforts.
Essential Job Duties
AI Solution Architecture and Engineering
- Design, develop, and deploy enterprise-grade AI applications using large language models, agentic frameworks, orchestration platforms, vector databases, and modern cloud-native architectures.
- Lead the technical design of AI systems from prototype through production deployment, ensuring scalability, reliability, observability, and maintainability.
- Build proof-of-concepts that validate emerging AI capabilities and rapidly assess their applicability to enterprise business challenges.
- Define and implement AI architecture patterns, reusable frameworks, reference implementations, and development standards for enterprise adoption.
- Develop sophisticated agentic solutions that automate complex business workflows spanning multiple enterprise systems and data sources.
Advanced AI Development
- Design and implement retrieval-augmented generation (RAG) architectures, vector indexing strategies, and knowledge-grounded AI solutions.
- Develop agent orchestration frameworks, Model Context Protocol (MCP), tool-calling architectures, and workflow automation platforms.
- Engineer prompt strategies, evaluation frameworks, guardrails, and testing methodologies to optimize model accuracy, reliability, and safety.
- Evaluate and integrate emerging AI technologies, foundation models, embeddings, and enterprise AI platforms into the Institute's technology ecosystem.
- Establish performance benchmarks and quality metrics for AI systems, agents, and workflows.
Enterprise Integration and Platform Engineering
- Architect and implement secure integrations between AI applications and enterprise systems, including HR, Finance, Facilities, Student Affairs and Research Administration platforms.
- Design APIs, event-driven architectures, middleware services, and data pipelines that support enterprise-scale AI deployments.
- Develop secure data access patterns for AI workloads while ensuring compliance with institutional data governance requirements.
- Optimize enterprise AI systems for performance, cost efficiency, reliability, and operational supportability.
- Lead deployment automation, monitoring, troubleshooting, and lifecycle management of AI services in production environments.
Technical Leadership
- Demonstrate technical leadership on Enterprise AI architecture, engineering patterns, and solution design.
- Perform architecture reviews, code reviews, prompt reviews, and design reviews for AI-related initiatives.
- Mentor software engineers and solution architects in AI engineering practices and emerging technologies.
- Establish AI engineering standards, development frameworks, testing methodologies, and operational best practices for enterprise AI solutions.
- Provide technical recommendations regarding AI technology selection, architecture tradeoffs, and platform investments.
Basic Qualifications
- Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Information Systems, or a related technical discipline, or equivalent practical experience.
- Extensive experience designing and delivering large-scale enterprise software solutions in complex environments.
- Demonstrated expertise building production Generative AI applications using large language models and agentic frameworks.
- Strong software engineering experience with modern programming languages such as Python, Java, C# or similar languages.
- Deep understanding of AI/ML frameworks, model deployment platforms, API design, cloud-native architectures, and distributed systems.
- Hands-on experience implementing Retrieval-Augmented Generation (RAG), vector databases and knowledge-grounded AI systems.
- Experience designing agent architectures, agent orchestration frameworks, and Model Context Protocol (MCP) based integrations.
- Demonstrated experience integrating AI solutions with enterprise ERP systems such as Oracle E-Business Suite and other enterprise platforms.
- Strong understanding of enterprise security architecture, identity management, data privacy, and data loss prevention controls.
- Proven ability to bring AI systems from concept and experimentation through production deployment and operational support.
- Experience deploying AI solutions on Microsoft Azure OpenAI, AWS Bedrock, Google Vertex AI, or comparable enterprise AI platforms.
Preferred Qualifications
- Experience with containerization, Kubernetes, CI/CD pipelines, infrastructure-as-code, and modern platform engineering practices.
- Familiarity with higher education, research administration, healthcare, or similarly complex data-intensive environments.
Required Documents
- Resume.
To be considered for this position please visit our web site and apply on line at the following link: https://hr.caltech.edu/work/job_openings
We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.
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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 California Institute of Technology, 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.
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.
California Institute of Technology AI Hiring
California Institute of Technology has 2 open AI roles right now. They're hiring across Research Engineer, AI Architect. Based in Pasadena, CA, US.
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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