Principal AI Infrastructure Engineer

** Principal AI Infrastructure Engineer, Developer Experience
** This role is within the global Developer Experience (Dev Ex) team, specifically the Model Developer Experience (MDX) sub-team – whose mission is to deliver world-class infrastructure and platforms that enable AI builders across ADI to move faster, with confidence, and  will join a high-performance, mission-driven interdisciplinary team that spans data science, software engineering, platform architecture, cloud infrastructure, and security expertise.

We believe in a culture founded on trust, mutual respect, growth mindsets, and an obsession for building extraordinary products with extraordinary people.
** Role Summary
** As a Principal AI Infrastructure Engineer (individual contributor), you will be part of the team that is the recognized technical authority and strategic thought leader in AI infrastructure across ADI's diverse deployment environments. You will help set the long-term vision and architectural direction for how the organization's AI infrastructure evolves to support breakthrough applications in physical intelligence and Intelligent Edge systems.

You will help lead cross-team initiatives that elevate AI developer experience and infrastructure capabilities org-wide, translate emerging business needs into scalable platform strategies, and mentor engineers in shaping the future of AI infrastructure r impact will be defined by your ability to see patterns across the organization, design elegant solutions that serve multiple constituencies, and build durable frameworks and standards that amplify the capabilities of every AI builder  will work at the intersection of research, technical architecture, and developer enablement—translating research and product goals into infrastructure systems that reduce complexity and accelerate innovation.
** Key Responsibilities*
* ** AI Developer Experience
** Help define and evolve the organizational vision for AI developer experience, identifying gaps and opportunities across the full AI development lifecycle and across all deployment environments.

Lead cross-team initiatives that systematically improve how AI builders experience infrastructure—from experimentation through production—creating measurable improvements in developer productivity and satisfaction. Architect the organization's long-term AI infrastructure strategy spanning on-premise, hybrid, and cloud-native environments, ensuring cohesive developer experience across all.

Lead the design of scalable, multi-region AI infrastructure that supports ADI's geographic expansion and business unit diversity, accounting for regulatory, latency, and cost requirements.
** Required Skills & Experience
** Recognized expert in AI infrastructure with deep knowledge of on-premises, hybrid, and cloud-native architectures, with demonstrated influence and impact across organizations or business units.

Expert communication and strategic thinking skills—ability to translate technical architecture into research and product impact.

Expert-level proficiency with Kubernetes, distributed compute frameworks (Ray, Spark, or equivalent), and the ability to define org-wide orchestration and scheduling strategies. ability to design reusable, multi-team infrastructure Expert knowledge of cloud platforms (AWS, Azure, or equivalent) and proven ability to architect multi-cloud or hybrid strategies that balance flexibility, cost, and operational complexity. Demonstrated ability to lead large, cross-team initiatives from conception through execution, influencing complex decision-making and shaping long-range technical directions.

Recognized ability to drive innovation, anticipate organizational needs, and architect durable solutions that scale with the business.
** Preferred Skills – Physical Intelligence and Industrial AI
** Expert-level knowledge of ROS/ROS2 ecosystems and the infrastructure challenges of deploying and managing ML models across diverse robotic platforms and environments. Strategic experience with edge AI deployment and the architectural tradeoffs between centralized cloud inference, edge inference, and hybrid models in physical systems.  architectures…