Help us accelerate the adoption of the Apple Data Platform (ADP) across Apple! ADP brings together the Apple Cloud Data Platform (ACDP), Apple Cloud AI (ACAI), and Apple Governance Platform (AGP) to provide foundational capabilities for data, analytics, AI, and governance at Apple scale.
We are looking for a highly technical and customer-focused Senior Engineering Manager to lead our Solutions Engineering organization. This team serves as the technical bridge between Apple's customers and ADP engineering teams, helping teams architect and productionize solutions spanning large-scale data processing, machine learning, Generative AI, LLMs, agents, and governance.
This leader will also serve as a senior technical representative for ADP with executive stakeholders across Apple, building trusted relationships, understanding their strategic priorities, representing ADP's capabilities and roadmap, and driving alignment across complex cross-organizational initiatives. The ideal candidate combines deep technical expertise with exceptional executive presence, communication, and stakeholder management skills, and can move seamlessly between deep technical discussions and strategic executive conversations.
Description
As the Senior Engineering Manager for Solutions Engineering within the Apple Data Platform organization, you will build and lead the team responsible for driving successful technical adoption of ADP across Apple.
Your team will engage with customers throughout their journey from understanding business and technical requirements and defining end-to-end architectures through onboarding, integration, migration, optimization, and production readiness. Solutions Engineering will work across ACDP, ACAI, and AGP, helping customers bring together data, compute, AI, and governance capabilities to build everything from large-scale analytics and machine learning pipelines to Generative AI applications, LLM-powered experiences, and agentic systems.
This is a highly technical and hands-on leadership role. You and your team will work directly with customers on complex architectures and production challenges while partnering closely with ADP engineering teams to identify platform gaps and influence the evolution of the platform.
A critical part of the team's mission is creating leverage across ADP. Rather than repeatedly solving the same problem for individual customers, the team will identify common patterns and turn successful solutions into reference architectures, integrations, automation, tooling, best practices, and reusable platform capabilities.
Minimum Qualifications
10+ years of experience with large-scale distributed systems, cloud infrastructure, data platforms, or AI/ML technologies, including 5+ years of engineering management experience.
Deep technical expertise in distributed systems, cloud-native architectures, and modern data and AI platforms.
Experience architecting and productionizing large-scale data, ML, Generative AI, LLM, or agentic workloads.
Demonstrated experience working directly with customers to solve complex technical problems and drive production adoption.
Exceptional executive presence, communication, and stakeholder management skills, with experience representing technology organizations with senior executive stakeholders.
Proven ability to influence and drive alignment across complex, cross-functional engineering organizations.
Proven ability to recruit, mentor, and develop senior technical talent.
BS or MS in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
Preferred Qualifications
Experience building, operating, or driving adoption of enterprise-scale data, AI, Generative AI, or governance platforms.
Experience with technologies such as Kubernetes, Spark, Ray, Kafka, Trino, Iceberg, and modern AI/ML infrastructure.
Experience architecting LLM applications, agentic systems, RAG, model serving, and AI orchestration.
Experience with public cloud platforms such as AWS and GCP, and serverless or multi-tenant platform architectures.
Experience developing reference architectures, integrations, automation, and reusable solutions that accelerate customer adoption.
Experience with AI governance, security, observability, and operating production AI systems at scale.
