Role Description - Define and execute the organization's enterprise AI vision, strategy, and operating model. - Develop a multi-year roadmap for AI adoption, platform investments, business enablement, and technology modernization. - Establish governance, funding, prioritization, and success metrics for AI initiatives. - Build and manage a balanced portfolio of AI programs that drive business value while maintaining appropriate operational, security, and risk controls. - Evaluate emerging technologies and identify opportunities to improve business performance, customer experiences, operational efficiency, and employee productivity. - Serve as a trusted advisor to executive leadership on AI strategy, opportunities, risks, and industry trends. - Lead the development of enterprise AI platform capabilities that support scalable, secure, and reusable AI solutions. - Define architectural standards for intelligent applications, automation platforms, digital assistants, enterprise knowledge solutions, and AI-enabled workflows. - Establish foundational capabilities supporting: - Model access and orchestration - Knowledge retrieval and search - Enterprise data integration - Monitoring and observability - Security and governance controls - Drive adoption of AI-assisted software development practices across the software delivery lifecycle. - Partner with architecture, engineering, infrastructure, operations, security, and data teams to modernize technology delivery through AI-enabled capabilities. - Collaborate with business leaders to identify and prioritize high-value AI use cases. - Lead initiatives focused on: - Intelligent automation - Decision support systems - Document and knowledge management - Workflow optimization - Productivity enhancement - Customer and employee experience improvements - Develop frameworks for evaluating, prioritizing, and scaling AI opportunities across the organization. - Establish adoption strategies, change management programs, and success measurements for enterprise AI initiatives. - Build organizational AI literacy through education, enablement programs, and communities of practice. - Partner with risk, security, legal, compliance, and data governance stakeholders to ensure responsible AI adoption. - Embed security, privacy, transparency, human oversight, and operational controls into AI platforms and business solutions. - Support the development and operationalization of enterprise AI governance frameworks. - Balance innovation and experimentation with appropriate governance and risk management practices. - Promote ethical and responsible use of AI technologies across the organization. - Lead delivery of enterprise AI initiatives from strategy through implementation and adoption. - Establish delivery frameworks, operating models, and performance measures that ensure successful execution. - Manage technology investments, budgets, vendor relationships, and strategic partnerships. - Drive accountability for scope, timelines, quality, adoption, and business outcomes. - Ensure AI initiatives are scalable, supportable, and aligned with enterprise architecture and operational standards. - Build and lead a high-performing team of AI, engineering, architecture, automation, and technology professionals. - Foster a culture of innovation, experimentation, accountability, and continuous learning. - Mentor leaders and technical teams while establishing clear goals, priorities, and performance expectations. - Serve as a catalyst for organizational change and technology transformation. - Promote collaboration across business and technology functions to accelerate enterprise adoption. Qualifications - 15+ years of progressive leadership experience in technology, digital transformation, software engineering, architecture, data, automation, or emerging technologies. - Significant experience leading enterprise-scale AI, automation, analytics, or digital transformation initiatives. - Demonstrated success building and executing technology strategies that deliver measurable business outcomes. - Experience operating within highly regulated or complex enterprise environments preferred. - Strong understanding of modern AI and Generative AI technologies, including: - Large language models and foundation models - AI orchestration and workflow automation - Retrieval and knowledge-based architectures - Prompt engineering and evaluation methodologies - AI application development patterns - Experience with intelligent automation, AI-enabled software development, and enterprise AI platforms. - Familiarity with responsible AI principles, governance frameworks, model lifecycle management, and operational monitoring. - Strong technical foundation across cloud platforms, APIs, distributed systems, data architecture, cybersecurity, identity, and modern engineering practices. - Proven ability to align technology investments with strategic business objectives. - Strong executive communication and stakeholder management skills. - Experience influencing senior leadership and driving enterprise-wide change initiatives. - Demonstrated success managing teams, vendors, consulting partners, and complex delivery portfolios. - Strong financial acumen, including budgeting, investment planning, vendor management, and value realization. Education - Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related discipline required. - Advanced degree preferred. Leadership Characteristics - Strategic thinker with a passion for innovation and emerging technologies. - Hands-on leader who balances vision with execution. - Strong business acumen combined with deep technical credibility. - Collaborative influencer who builds alignment across diverse stakeholder groups. - Results-oriented operator focused on measurable outcomes and sustainable transformation. - Coach and mentor committed to developing talent and building high-performing teams.

• Establishes the architectural patterns, evaluation practices, and deployment standards for the team • Makes framework and model recommendations that set the foundation for how the team builds — evaluates orchestration frameworks, selects deployment patterns, trains and fine-tunes models, and determines where managed platforms end and custom build begins • Translates proposed business solutions into technical plans — defines product life cycles, prioritizes the backlog, and breaks initiatives into buildable work • Owns solutions end-to-end: technical planning, architecture, build, deploy, and the monitoring that keeps them honest in production • Builds the monitoring, evaluation, and regression detection systems that keep production agents reliable — including logging, performance benchmarking, and feedback loops that surface drift early • Partners with data governance to ensure solutions meet compliance, data quality, and operational standards

• Own the technical architecture of the XR Training Academy platform • Define and maintain architecture standards across frontend, backend, AI, cloud, and Unity integrations • Create technical roadmaps aligned with product goals • Lead technical planning and solution design sessions • Review technical approaches and ensure alignment with business requirements • Prevent over-engineering and unnecessary technical complexity • Design scalable cloud-native architectures • Define APIs, integration patterns, and service boundaries • Ensure performance, security, maintainability, and scalability considerations are built into solutions • Guide AI architecture and integration decisions • Evaluate and recommend technology choices • Translate product requirements into technical implementation plans • Break epics and features into technical workstreams • Define acceptance criteria and technical success metrics • Identify technical risks and mitigation strategies • Support estimation and sprint planning activities • Guide the implementation of AI-powered experiences • Evaluate and integrate LLMs, AI services, and agent-based workflows • Establish best practices for AI architecture, prompting, evaluation, and governance • Coach team members on the effective use of AI-assisted development • Contribute production-quality code • Build backend services, APIs, integrations, and cloud infrastructure • Support frontend and platform development as required • Review pull requests and mentor developers • Troubleshoot technical issues and support delivery teams • Foster strong communication and accountability

• Work directly with teams and customers to identify high-impact opportunities for AI and workflow automation. • Design, build, and deploy AI agents and intelligent systems into production environments. • Lead discovery sessions to understand workflows, pain points, and business requirements. • Translate complex business challenges into practical technical solutions. • Rapidly prototype ideas and iterate based on real-world feedback. • Integrate AI solutions with existing systems, applications, and data sources. • Build reusable frameworks and components that accelerate future AI initiatives. • Monitor adoption, performance, and effectiveness of deployed solutions. • Collaborate across technical and non-technical teams to deliver meaningful outcomes. • Stay current with emerging AI technologies and identify opportunities to apply them.