Leads enterprise direction for AI-specific platforms and shared products whose primary purpose is predictive, generative, or agentic artificial intelligence and automation. The role owns accountable platform administration, product and service direction, strategic model-provider and vendor relationships, shared-capability roadmaps, and AI-platform configuration and integration requirements. The Executive Director builds non-production prototypes and reference implementations and works through Enterprise Architecture and Engineering for production architecture, platform engineering, DevOps, integration, deployment, operations, and support.
- Develop and maintain the enterprise AI-platform and shared-product strategy, investment principles, service model, scope criteria, and multi-year roadmap; distinguish AI-specific platforms from general enterprise applications with embedded AI and from Engineering-owned platform and DevOps tooling.
- Formalize accountable administration for in-scope AI and automation platforms, including ownership, environments, access, provisioning, configuration, capacity, cost, service measures, escalation, and lifecycle documentation.
- Define AI-product and AI-platform reference patterns, integration requirements, reusable components, interoperability standards, lifecycle patterns, and technical decision criteria in partnership with Enterprise Architecture and Engineering; those teams retain enterprise architecture and production engineering accountability.
- Own product direction for shared AI platforms and capabilities, including user needs, service definitions, roadmap priorities, quality, adoption, feedback, and lifecycle decisions.
- Create and govern a configuration and integration roadmap that enhances enterprise tools and workflows while aligning with data, identity, security, privacy, and responsible-AI requirements.
- Lead technical and commercial evaluation, build-versus-buy-versus-partner recommendations, and strategic relationships with AI platform, model, automation, and shared-service providers.
- Personally design, configure, prototype, and validate non-production reference implementations; provide Engineering with clear requirements, patterns, evidence, and acceptance criteria through build, deployment, observability, transition, and operational readiness. Engineering retains production code, integration, deployment, run, and support.
- Build and lead a high-leverage, AI-first team that creates reusable capabilities, actively engages enterprise stakeholders, demonstrates measurable platform value, and models a build-and-show culture; lead function operations, talent, performance, budgets, licensing, capacity, and resourcing in accordance with enterprise policy.
- Performs other duties as assigned.
Knowledge, Skills, and Abilities: - Deep hands-on expertise in enterprise AI, machine learning, generative AI, agentic systems, cloud, data, integration, automation, or related platform technologies. [Required]
- Strong working knowledge of solution architecture, APIs, data and semantic patterns, identity and access, security, privacy, observability, reliability, scalability, and production readiness, with the judgment to distinguish AI-product ownership from Enterprise Architecture and Engineering accountability. [Required]
- Ability to define AI-platform and product strategies, roadmaps, administration models, service measures, reference patterns, integration requirements, reusable capabilities, and technical decision criteria. [Required]
- Working knowledge of model, prompt, and agent lifecycles; evaluation; guardrails; monitoring; vendor-model dependencies; and responsible-AI-by-design. [Required]
- Strong product-management and customer-value orientation across discovery, service definition, prioritization, quality, adoption, and lifecycle decisions. [Required]
- Financial and commercial acumen covering licensing, consumption, capacity, total cost, vendor performance, strategic model-provider relationships, investment choices, and sourcing decisions. [Required]
- Ability to lead multidisciplinary technical teams and establish explicit decision rights, service interfaces, and handoffs with Engineering, Enterprise Architecture, data, security, privacy, governance, and vendors through the delivery lifecycle. [Required]
- Hands-on building, first-principles problem solving, lean-management discipline, technical and business-value storytelling, active executive engagement, and force-multiplier people leadership that produces reusable capabilities and measurable outcomes. [Required]
Education: - Master's degree [Required]
Field of Study: - In Information Technology, Computer Science, Information Systems, Engineering, or a closely related technical discipline [Required]
Work Experience: - 10+ years of relevant experience [Required]
- 5+ years of leadership experience [Required]
Physical Requirements: (Please click the link below to view work requirements)
- Physical Requirements - https://tinyurl.com/23km2677
Pay Range:$170,060.80 - $316,284.80
