Director, AI & Data Platforms

Teacher Retirement System of Texas (TRS)•Austin, TX

2d•Onsite

About The Position

The Director of AI & Data Platforms is responsible for leading the enterprise strategy, architecture, engineering, and operations of TRS’s artificial intelligence and data platforms. The incumbent will own the shared AI and data foundations that enable scalable, secure, and compliant analytics and AI capabilities across the organization, and ensure that platform capabilities, governance enforcement, and architecture standards are consistently applied to support business-driven data delivery and AI initiatives. This role is a key member of IT leadership and serves as the enterprise point of accountability for AI and data platform execution.

Requirements

Bachelor’s degree from an accredited college or university in Computer Science, Information Systems, Engineering, or a closely related field.
High school diploma or equivalent and additional full-time experience in data platforms, cloud architecture, IT enterprise or related experience may be substituted on an equivalent year-for-year basis.
Eight (8) years of full-time directly related, progressively responsible experience in data platforms, cloud architecture, IT enterprise or related experience.
Four (4) years of experience leading, or supervising the work of others, required.
Experience with enterprise AI platforms and agentic frameworks (e.g., Azure AI Foundry, Azure OpenAI Service, Copilot Studio, Databricks Model Serving).
Experience in enterprise AI adoption practices through agentic frameworks (LangChain, Semantic Kernel/Microsoft Agent Framework, Azure AI Agent Service), RAG pipeline design, MCP integrations, prompt engineering and responsible AI frameworks, AI guardrails and content safety.
A master's degree or doctoral degree in a closely related field may be substituted on an equivalent year-for-year basis.
None.

Nice To Haves

Knowledge of Data governance, security, and compliance frameworks.
Knowledge of Modern data platforms (e.g., Databricks, Azure, Fabric, Snowflake).
Knowledge of People leadership in the data platform, data engineering and data operations space.
A diverse base of knowledge that allows you to help your team solve complex technical problems.
Knowledge of The principles, practices, and techniques of computer programming and systems analysis and design; all phases of software development and project management; and computing standards and development methodologies, including Systems Development Life Cycle methodologies.
Knowledge of Varying technological architectures, security, and technology trends.
Knowledge of Agency computing standards, security, and development methodologies.
Skills in Leading large-scale data and AI platform initiatives
Skills in Multi-agent orchestration patterns (sequential, concurrent, handoff) and agent governance/observability tooling.
Skills in Leading analytics project teams; and organizing, managing, and motivating staff to meet project goals and objectives.
Skills in Analyzing problems and devising innovative and effective solutions, including collecting and analyzing complex data, evaluating information and business processes, and drawing logical conclusions.
Skills in Designing complex computer programs.
Skills in Project planning and management; planning, organizing, and coordinating work assignments to effectively meet frequent and/or multiple deadlines; handling multiple tasks simultaneously; and managing conflicting priorities and demands.
Skills in Communicating complex technical information to people of varying technical backgrounds.
Ability to Establish and maintain harmonious working relationships with co-workers, agency staff, and external contacts.
Ability to Work effectively in a professional team environment.
Ability to Plan strategically and align with peers in complex technical and operating environments.

Responsibilities

Builds and leads a high-performing team of platform engineers, architects, AI engineers, and governance specialists.
Develops workforce capabilities in AI and data platform engineering.
Partners with IT leadership on organizational strategy and staffing growth.
Directs department staff, directly and through team leaders, including hiring, directing, monitoring, evaluating, and motivating staff.
Establishes clear career paths and role specialization within AI and data platform domains.
Provides direction, monitors team work loads and work processes, and takes corrective actions as needed to ensure that all operations are covered, and productivity, customer service, and quality goals are met.
Ensures compliance with applicable federal, state, agency, and department policies, procedures, rules, and regulations.
Assesses training needs of team members and arranges for or provides training, coaching, and technical assistance.
Serves as the enabling technology partner for Data Delivery and LOB teams.
Provides “paved roads” for AI and data delivery teams.
Supports developer experience, onboarding, and platform adoption.
Reduces duplication and tool sprawl across the enterprise.
Partners closely with Enterprise Technology Services to achieve enablement goals.
Defines and execute the strategy for enterprise AI and data platforms (e.g., Fabric, Databricks, Azure AI services).
Oversees platform architecture, engineering, and lifecycle management.
Ensures scalability, reliability, performance, and cost optimization (FinOps).
Establishes and maintains enterprise AI and data platform architecture standards.
Ensures the platform provides the infrastructure and tooling to support AI-ready data patterns (e.g., semantic layers, RAG, data integration patterns).
Drive consistency in interoperability and platform alignment.
Owns platform architecture across the AI and data estate; sets architectural direction for how AI capabilities are built, deployed, and governed on shared infrastructure.
Owns enterprise data governance tooling and enforcement mechanisms, including data catalog (Purview or equivalent), data classification and sensitivity labeling, access controls and policy enforcement.
Ensure governance is implemented through platform capabilities, not manual processes.
Partner with enterprise governance bodies to align policy with technical enforcement.
Leads platform operations including monitoring, incident management, and reliability.
Owns environment strategy (sandbox, development, testing, production).
Establishes DevOps practices including CI/CD, version control, and deployment standards.
Ensures secure, compliant, and auditable platform usage.
Leads development of reusable AI capabilities (e.g., agents, copilots, orchestration frameworks).
Supports AI experimentation, R&D, and transition to production-ready capabilities.
Delivers shared services and patterns that enable downstream delivery teams.
Performs related work as assigned.