• Own documentation as a system: the pipelines that build and publish it, the AI-augmented workflows that generate drafts for human review and refinement, the review and publish process, and the infrastructure. • Lead the growth of existing docs-as-code foundation and AI-assisted documentation workflows into a docs-as-AI-agents capability. • Own documentation site and its publishing as software: docs-as-code repo, CI/CD publishing pipelines, build performance, and automated checks. • Build AI-augmented documentation workflows: drafting, summarization, classification, consistency and staleness checks, and feedback loops that improve generation quality over time. • Generate reference documentation from source (OpenAPI and related specs) and keep docs in sync with platform changes. • Lower the barrier for internal contributors to ship their own docs through the docs-as-code workflow. • Own release-notes and customer communications cadence with every platform release. • Maintain documentation style guide and ensure the function is not dependent on any one person.

• Own documentation as a system: the pipelines that build and publish it, AI-augmented workflows for drafts, review and publish process, and infrastructure for AI agents. • Take existing docs-as-code foundation and grow it into a docs-as-AI-agents capability. • Own the documentation site and its publishing as software: docs-as-code repo, CI/CD pipelines, automated link, structure, and quality checks. • Grow AI-augmented documentation workflows: AI-assisted drafting, summarization, consistency checks, and feedback loop improvements. • Structure content for AI systems consumption and maintain interfaces for agents. • Generate reference documentation from source and keep it aligned with code changes. • Lower barrier for internal contributors to ship their own docs through automation. • Own release-notes and customer communications cadence. • Maintain the documentation style guide and team runbook.

• Lead the deployment of sophisticated AI models and applications using state-of-the-art tools and platforms. • Design and Develop AI solutions specifically harnessing the power of LLMs. • Work with industry-standard LLM APIs to inject cutting-edge AI functionalities into broader applications using your experience with cloud platforms like AWS and Azure. • Create models that drive business outcomes and make iterative improvements based on data-driven insights. • Ensure deployed models operate at peak efficiency, requiring a vigilant approach to performance monitoring and adjustment. • Deploy models within an Agile development framework, showcasing flexibility, rapid iteration, and a commitment to constant improvement. Proactively collaborate within the Applied AI team and cross-functionally with other development teams, product and business stakeholders to iterate on ideas, solve problems, and achieve shared goals.

• Ensure AI tools are consistently used and integrated into daily work, not just explored; Identify and remove barriers to adoption • Deliver enablement at scale, including role-based learning journeys, toolkits, office hours/coaching, and a champion network in partnership with training and operations • Help establish AI COE framework to promote responsible AI use, best practices, training and enablement programs, success metrics, and communication • Partner with training and operations teams to develop role-based enablement across the enterprise (sales, service, ops, leadership) • Equip teams to effectively use AI platforms, Copilot tools, AI agents, and prompt & agent libraries • Provide ongoing support through office hours, coaching, and trainings materials • Reinforce expectations for when and how AI should be used; Ensure responsible AI usage by incorporating governance and guardrails into training, communications, and playbooks • Partner with business and operations teams to roll out AI capabilities and integrate them into daily work. Develop change management strategies and educate teams on AI benefits. • Identify where AI can meaningfully improve workflows, quality, and speed; Help define roles of AI (automate, augment, or escalate) vs human • Ensure AI usage is clear, repeatable, and consistent to shift from manual to AI-assisted workflows • Understand pain points and translate needs into clear, actionable AI use cases; Identify where AI can deliver meaningful workflow improvements and practical impact • Bring structured recommendations to inform prioritization and sequencing of AI efforts • Act as a bridge between business users and AI/product teams to ensure solutions reflect real needs, are adopted, and deliver value; Provide insight into what will drive adoption and proactively identify where solutions are likely to stall • Support business teams in owning outcomes and driving adoption; Ensure success metrics are defined and use data to continuously refine prioritization and enablement strategies • Gather feedback from users on what’s working, where friction exists, and where trust is low • Partner with engineering to improve usability and effectiveness of AI solutions • Continuously refine enablement materials, guidance, and approach • Enable acceptable AI usage, citizen development, and sharing & reuse of AI tools • Track and report AI impact • Share success stories, use cases, best practices, and lessons learned to accelerate adoption across teams.