AI Enablement Lead

Imagine what you could do here! At Apple, new ideas have a way of becomingextraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job, and there's no telling what you could accomplish. Thepeople here at Apple don’t just craft products - they build the kind of wonder that’srevolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts.

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. This 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 scale.

This is a high-ownership, high-visibility role that will help influence how the People organization evolves its operational model over time.

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

• Deep technical understanding of modern AI application architecture, including LLM-powered applications, tool calling, retrieval and grounding, context management, structured outputs, and agentic workflow orchestration.
• Experience 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 systems.
• Experience building or integrating solutions using APIs, MCP servers, orchestration frameworks, enterprise integration layers, and reusable agent/tool interfaces.
• Understanding of enterprise hosting and deployment patterns for AI-enabled applications, including runtime environments, environment separation, scalability, reliability, configuration management, and production support.
• Strong 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 handling.
• Experience with AI evaluation and observability practices, including tracing, logging, regression testing, quality evaluation, latency and reliability monitoring, failure analysis, and production telemetry.
• Understanding of state management, retries, fallbacks, exception handling, escalation paths, and long-running workflow design for production agentic systems.
• Experience working with structured and unstructured enterprise data, including data access patterns, schemas, SQL, retrieval pipelines, knowledge repositories, and enterprise data platforms.
• Familiarity with software engineering practices such as source control, CI/CD, automated testing, release management, environment management, and operational support.
• Experience identifying reusable AI capabilities, integration patterns, shared services, and platform components that can scale across multiple business functions.
• Strong understanding of responsible AI, privacy, governance, and control requirements in environments involving sensitive employee or enterprise data.
• Experience with in People, HR technology, People Operations, People Support, or adjacent enterprise business functions is a plus.