People AI Enablement Lead

Imagine what you could do here! At Apple new ideas have a way of becomingnextraordinary products services and customer experiences very quickly. Bring passionnand dedication to your job and theres no telling what you could accomplish. Thenpeople here at Apple dont just craft products - they build the kind of wonder thatsnrevolutionized entire industries. Its the diversity of those people and their ideas thatninspires the innovation that runs through everything we do from amazing technology tonindustry-leading environmental efforts.n

The People Technology team is looking for an AI Enablement Lead to partner with our business owners and engineering teams on re-engineering processes using new emerging technology. nnThis role sits squarely at the intersection of business strategy technology process design and applied AI. The ideal candidate combines operational judgment with technical fluency understanding both the realities of People processes and the practical considerations required to deploy AI-enabled workflows responsibly at enterprise is a high-ownership high-visibility role that will help influence how the People organization evolves its operational model over time.n

Conduct structured assessments across People functions to identify workflows where AI-enabled automation can deliver meaningful improvements in user experience effectiveness scalability and accuracynEvaluate opportunities based on operational leverage business value process complexity risk profile governance considerations and measurable impactnIdentify repetitive manual work operational bottlenecks fragmented workflows and high-volume process areas suitable for intelligent automationnBuild and maintain a prioritized backlog and roadmap of AI deployment opportunities tied to clearly defined operational KPIs and business outcomesnMap structured and unstructured data flows across enterprise platforms including Workday ServiceNow People EDW collaboration platforms project management tooling and knowledge repositoriesnDefine human-in-the-loop review checkpoints escalation paths auditability requirements and governance controls within deployed workflowsnConfigure and operationalize AI-enabled workflows using APIs MCP servers orchestration tooling integration layers and enterprise operational platformsnTranslate operational requirements into scalable production-ready solutions with appropriate safeguards monitoring documentation and support modelsnEnsure deployed solutions remain maintainable supportable and operationally sustainable over timenOperate and monitor deployed agents and AI-enabled workflows against defined operational KPIs including cycle time exception rates accuracy reliability adoption and workflow qualitynManage evaluations regression testing and workflow validation following significant model updates schema changes process modifications or operational dependency changesnMaintain operational documentation including workflow maps data lineage escalation models governance considerations and change logsnPartner cross-functionally with People team leadership operations teams engineering governance security and platform teams to ensure deployed workflows align with enterprise standards and operational controlsnSurface emerging automation opportunities as operational needs and organizational priorities evolvenDrive iterative improvement of deployed workflows through operational feedback loops usage patterns testing and ongoing refinementnSupport adoption of AI-enabled workflows through rollout planning stakeholder engagement operational enablement documentation and training supportnEstablish and maintain People-specific knowledge repositories including process documentation operational narratives runbooks LOB context and workflow guidancenEnsure knowledge assets remain current governed and accessible in ways that improve both workflow reliability and team self-service capabilitiesnContribute to operational best practices for deploying AI-enabled systems responsibly within enterprise People environmentsn

8 years of experience in enterprise technology AI enablement automation integrations digital transformation or related technical experience translating complex business and operational requirements into scalable technology experience designing building or enabling automated workflows across enterprise systems and business -on technical fluency with APIs integrations scripting SQL/data querying workflow orchestration and enterprise application working with AI-enabled applications agentic workflows or intelligent automation solutions in an enterprise understanding of software delivery and production lifecycle concepts including development testing deployment monitoring support and continuous partnering across engineering business security governance UX and platform teams to deliver enterprise to evaluate technical feasibility operational complexity business value risk and scalability when prioritizing automation communication and stakeholder-management skills with the ability to translate between technical teams and business to operate effectively in ambiguous environments independently shape problems and drive initiatives from discovery through implementation and adoption.n

Deep technical understanding of modern AI application architecture including LLM-powered applications tool calling retrieval and grounding context management structured outputs and agentic workflow designing agentic architectures such as tool-using agents orchestrator/worker patterns multi-agent workflows event-driven agents approval-based workflows and human-in-the-loop building or integrating solutions using APIs MCP servers orchestration frameworks enterprise integration layers and reusable agent/tool of enterprise hosting and deployment patterns for AI-enabled applications including runtime environments environment separation scalability reliability configuration management and production knowledge of security patterns for AI and enterprise applications including authentication authorization service identities secrets management least-privilege access secure API design auditability and sensitive-data with AI evaluation and observability practices including tracing logging regression testing quality evaluation latency and reliability monitoring failure analysis and production of state management retries fallbacks exception handling escalation paths and long-running workflow design for production agentic working with structured and unstructured enterprise data including data access patterns schemas SQL retrieval pipelines knowledge repositories and enterprise data with software engineering practices such as source control CI/CD automated testing release management environment management and operational identifying reusable AI capabilities integration patterns shared services and platform components that can scale across multiple business understanding of responsible AI privacy governance and control requirements in environments involving sensitive employee or enterprise within People HR technology People Operations People Support or adjacent enterprise business functions is a plus.