Enterprise AI Architect

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.

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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• Resume.