Enterprise AI Architect

Job Summary

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.

Hiring Range

• $251,000 to $300,000 per year.
• The salary of the finalist(s) selected for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, specialty and training.

Benefits

• Caltech offers competitive compensation, health, dental, and vision insurance, retirement savings plans, generous paid time off (vacation, holidays, sick time, parental leave, bereavement, etc.), tuition reimbursement, and more.
• Non-benefit eligible employees will have access to some benefits such as onsite counseling and sick time.

Required Documents

• Resume.