Senior Enterprise Architect - AI

Texas A&M University System • College Station, TX

2d

About The Position

The System Offices is one of several system members within the Texas A&M University System representing one of the largest systems of higher education in the nation, with a network of 12 universities, a comprehensive health science center, nine state agencies, and the RELLIS Campus. The Texas A&M University System mission is to provide education, conduct research, commercialize technology, offer training, and deliver services for the people of Texas and beyond. The System Offices, within the Texas A&M University System, provides an outstanding benefits package including, but not limited to: competitive health benefits; paid vacation, sick leave, and holidays; a defined benefit retirement plan to include an employer contribution through Teachers Retirement System of Texas (TRS); if applicable, a defined contribution retirement plan to include an employer contribution through an approved ORP vendor: additional voluntary tax deferred annuity (TDA) options; tuition assistance; and wellness programs to promote work/life balance. Salary: Commensurate with experience. Job Description Summary: The Senior Enterprise Architect - AI is responsible for role will work under the direction of the Executive Director, Enterprise Architecture and work closely with the OIT leadership team and staff members in developing AI solutions for the system office and for its members.

Requirements

• Master’s degree or Doctorate Degree in applicable field.
• Four years of related experience (master’s degree).
• Three years of related experience (Doctorate Degree).
• Extensive knowledge of artificial intelligence and machine learning concepts, including deep learning, neural network architecture, natural language processing (NLP), computer vision, reinforcement learning, and generative AI, with sufficient depth to engage in substantive technical dialogue with faculty researchers and AI scientists.
• Knowledge of large language model (LLM) architectures, training methodologies, fine-tuning techniques, embedding management, retrieval-augmented generation (RAG), prompt engineering, and the orchestration of multi-model and agentic AI systems.
• Knowledge of AI/ML model lifecycle management, including data preparation, feature engineering, model training, validation, deployment, monitoring, drift detection, retraining, and decommissioning. Understanding of MLOps practices and CI/CD pipelines for AI workloads.
• Knowledge of enterprise architecture principles, frameworks (e.g., TOGAF, Zachman), reference architecture, and governance models, and the ability to apply these in a complex, multi-institution higher education environment.
• Ability to multitask and work cooperatively with others across organizational boundaries.
• Ability to manage multiple concurrent AI initiatives in various stages of maturity, from early research collaboration through production deployment and ongoing operations.
• Ability to work beyond normal office hours and travel as needed to engage with member institutions, attend conferences, and participate in system-wide technology initiatives.
• Ability to interact effectively with top researchers, including AI researchers, general faculty, and staff across a large higher education system—understanding their research methodologies, computational needs, data requirements, and publication timelines—and to translate those needs into enterprise architecture decisions and shared infrastructure strategies.
• Ability to multi-task and work cooperatively with others.
• This is a security-sensitive position and is restricted to U.S. citizens and legal permanent residents only.
• Only complete applications will be considered for employment at The Texas A&M System Offices. Incomplete job application data could result in your application being rejected without an option to reapply.
• A cover letter and resume may be required in addition to a completed employment application.
• All positions are security-sensitive. Applicants are subject to a criminal history investigation, and employment is contingent upon the institution’s verification of credentials and/or other information required by the institution’s procedures, including the completion of the criminal history check.

Responsibilities

• AI Strategy and Enterprise Architecture - Working under the direction of the Executive Director, Enterprise Architecture, develop, maintain, and communicate a comprehensive AI and machine learning architecture strategy and multi-year roadmap aligned with the Texas A&M University System’s institutional mission, strategic priorities, and digital transformation goals.
• - Define and enforce enterprise architecture standards, reference architectures, design patterns, and governance frameworks for AI/ML solutions across the System Office and member institutions.
• - Evaluate emerging AI technologies, platforms, and methodologies; conduct feasibility assessments and proof-of-concept initiatives to determine applicability and readiness for enterprise adoption.
• - Advise the Executive Director of Enterprise Architecture and OIT leadership on the strategic integration of AI capabilities into the System’s technology portfolio, including build-versus-buy analyses and total cost of ownership assessments.
• AI Solution Design and Implementation - Architect end-to-end AI/ML solutions—including data pipelines, model training and validation workflows, deployment infrastructure, and monitoring systems—ensuring scalability, reliability, security, and reproducibility.
• - Design and oversee the implementation of generative AI, large language model (LLM), natural language processing (NLP), agentic AI, and intelligent automation solutions that address administrative, academic, and research use cases across the System.
• - Collaborate with data architects, software engineers, infrastructure teams, and data scientists to ensure AI solutions integrate seamlessly with existing enterprise platforms (e.g., ERP, CRM, LMS, research computing environments).
• - Establish and promote MLOps best practices, including CI/CD pipelines for AI models, automated testing and validation, model versioning, drift detection, retraining protocols, and lifecycle management.
• Cross-Institutional Collaboration and Stakeholder Engagement - Serve as a primary technical liaison between the System Office, member institution CIOs, IT leadership, and faculty researchers on the development and deployment of common AI solutions and shared services.
• - Engage with faculty members who research, use, and apply AI to identify collaborative opportunities, align institutional AI initiatives, and ensure enterprise architecture supports the academic and research missions.
• - Facilitate cross-functional architectural discussions, workshops, and working groups to build consensus, awareness, and alignment around AI strategies and technology standards across the System.
• - Translate complex technical AI/ML concepts into clear, accessible communications for executive leadership, non-technical stakeholders, governing boards, and diverse institutional audiences.
• AI Governance, Ethics, and Compliance - Working with the OIT Senior Leadership Team, develop and implement AI governance policies and frameworks that address responsible AI use, algorithmic bias mitigation, model explainability, transparency, and accountability in alignment with institutional values and regulatory requirements.
• - Ensure all AI architecture and solutions comply with applicable federal and state regulations, industry standards, and institutional policies, including FERPA, HIPAA, data privacy, cybersecurity requirements, and Texas Administrative Code provisions.
• - Define and enforce data quality, data governance, and information security standards specific to AI/ML workloads, including appropriate handling of sensitive research data and personal identifiable information.
• - Conduct architectural reviews, risk assessments, and security evaluations for AI solutions prior to deployment, and establish ongoing monitoring and audit protocols.
• Technical Leadership and Capacity Building - Provide technical leadership and mentorship to OIT staff, project teams, and member institution IT professionals on AI architecture principles, best practices, and implementation methodologies.
• - Develop and deliver training programs, workshops, documentation, and educational resources to build AI literacy and technical capacity across the System Office and member institutions.
• - Stay current with the rapidly evolving AI landscape, including advances in foundation models, generative AI, AI safety, responsible AI practices, and high-performance computing (HPC) for AI workloads.
• - Contribute to the professional development and knowledge-sharing culture within OIT by presenting at internal forums, participating in professional organizations, and publishing thought leadership where appropriate.
• Vendor and Platform Management - Evaluate, recommend, and manage relationships with AI technology vendors, cloud service providers, and platform partners to ensure the System receives optimal value, performance, and service levels.
• - Lead technical evaluations for AI-related procurements, including the development of requirements documentation, RFP technical criteria, vendor scoring rubrics, and selection recommendations.
• - Oversee the integration and orchestration of third-party AI services, APIs, and platforms within the enterprise technology ecosystem, ensuring interoperability, security, and adherence to architectural standards.
• Research Computing and AI Infrastructure - Collaborate with research computing teams and HPC centers to architect AI infrastructure solutions that support both administrative and research computing needs, including GPU-accelerated workloads, distributed training, and large-scale inference.
• - Advise on AI infrastructure capacity planning, including computer, storage, networking, and power requirements for current and future AI/ML workloads.
• - Support the development of shared AI computing resources and services that enable faculty, researchers, and institutional staff to leverage AI capabilities effectively and efficiently.

Benefits

• competitive health benefits
• paid vacation, sick leave, and holidays
• a defined benefit retirement plan to include an employer contribution through Teachers Retirement System of Texas (TRS)
• if applicable, a defined contribution retirement plan to include an employer contribution through an approved ORP vendor
• additional voluntary tax deferred annuity (TDA) options
• tuition assistance
• wellness programs to promote work/life balance

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level