People AI Enablement Lead

Apple•Austin, TX

About The Position

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.

Requirements

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.

Nice To Haves

Experience within People, HR technology, People Operations, People Support, or adjacent enterprise business functions is a plus.

Responsibilities

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