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PrincipalAI Infrastructure Engineer, Developer Experience

Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than$9 billionin FY24 and approximately24,000 peopleglobally, ADI ensures today's innovators stay Ahead of What's Possible. Learn more at www.analog.com and on LinkedIn and Twitter (X).

This role is within the global Developer Experience (DevEx) 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 at scale. You will join a high-performance, mission-driven interdisciplinary team that spans data science, software engineering, platform architecture, cloud infrastructure, and securityexpertise. We believe in a culture founded on trust, mutual respect, growth mindsets, and an obsessionforbuilding extraordinary products with extraordinary people.

Role Summary

As a Principal AI Infrastructure Engineer (individual contributor), you will bepart of the team that is therecognized technical authority and strategic thought leader in AI infrastructure across ADI's diverse deployment environments. You willhelpsetthe 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 willhelplead 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 at scale.

Your 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 at ADI. You will work at the intersection ofresearch, technical architecture, and developer enablement-translatingresearch and productgoals 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,identifyinggaps 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.

• Work strategically with data science teams, and research groups to understand emerging AI use cases and infrastructure needs at the frontier of the organization's work.

• Establish org-wide patterns, standards, and best practices for AI infrastructure that are adopted across multiple business units and geographies.

• Design and advocate for developer-first abstractions and platforms that absorb infrastructure complexity while enabling advanced customization for specialized use cases.

• Help evolvegovernance frameworks-including model versioning, experiment tracking, deployment workflows, and compliance standards-that scale with organizational growth without stifling innovation.

• Shape the organizational approach to infrastructure cost, performance, andreliabilitytrade-offs, ensuring alignment with businessobjectivesacross teams.

• Mentor and develop engineers across the organization, creating a culture of architectural excellence and infrastructure craftsmanship.

On-Prem, Hybrid, and Cloud AI Infrastructure Engineering

• Architect the organization's long-term AI infrastructure strategy spanningon-premise, hybrid, and cloud-native environments, ensuring cohesive developer experience across all.

• Define reference architectures and technical standards forcomputeorchestration, model serving, inference optimization, and resource management that guide platform development across teams.

• 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.

• Own infrastructure innovation initiatives that improve efficiency, reduce cost, and unlock new capabilities-including GPUutilizationoptimization, heterogeneouscomputestrategies (CPUs, GPUs, NPUs, FPGAs), and emerging accelerator technologies.

• Establish enterprise-level observability, governance, and security frameworks for AI infrastructure thatmaintaincompliance across diverse environments while enabling rapid iteration.

• Drive architectural decisions on critical infrastructure dependencies (Kubernetes strategies, container runtimes, distributed compute frameworks, model serving platforms, etc.), influencing multi-year technical roadmaps.

• Partner with infrastructure and cloud teams to evolve shared platforms and services that serve AI workloads, ensuring architectural alignment across the organization.

• Anticipate infrastructure, architectural, and organizational risks-from evolving workload patterns to regulatory changes to emerging security threats-and implement durable solutions adopted org-wide.

• Lead by example in creating reusable Infrastructure-as-Code frameworks, architectural patterns, and tooling that amplify team productivity and reduce toil across the organization.

Required Skills & Experience

• Recognized expert in AI infrastructure with deep knowledge of on-premises, hybrid, and cloud-native architectures, withdemonstratedinfluence and impact across organizations or business units.

• Proventrack recordof architecting infrastructure systems that serve multiple, sometimes competing, organizational needs whilemaintainingcoherence and simplicity.

• Expertcommunication and strategic thinking skills-ability to translate technical architecture intoresearch and productimpact.

• Expert-levelproficiencywith Kubernetes, distributed compute frameworks (Ray, Spark, or equivalent), and the ability to define org-wide orchestration and scheduling strategies.

• Mastery of Infrastructure-as-Code andGitOpsframeworks, withdemonstratedability to design reusable, multi-team infrastructurepatternsand platforms.

• Deepexpertisein GPU and accelerator resource management, cost optimization, and performance tuning across diverse workload types and hardware configurations.

• 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.

• Strong background in distributed systems design, including handling scale, reliability, consistency, and failure modes across heterogeneous infrastructure.

• Demonstrated ability to lead large, cross-team initiatives from conception through execution, influencing complex decision-making and shaping long-range technical directions.

• Strong mentoring orientation withdemonstratedsuccess developing leaders and upskilling teams across the organization in infrastructure and platform topics.

• Recognized ability to drive innovation,anticipateorganizational needs, andarchitectdurable solutions that scale with the business.

Preferred Skills - Physical Intelligence and Industrial AI

• Deepexpertisein building or scaling AI infrastructure for robotics, autonomous systems, or industrialperceptionat enterprise scale, withdemonstratedpatterns and reusable frameworks.

• 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.

• Background designing ML infrastructure that supports rapid adaptation, few-shot learning, and task transfer in physical systems, enabling scalable deployment across heterogeneous environments.

• Deep understanding of heterogeneouscomputearchitectures (CPUs, GPUs, TPUs, NPUs, FPGAs) and experienceoptimizinginference pipelines for specialized hardware.

• Experience with real-time operating systems and the infrastructure requirements of hard real-time, safety-critical AI systems.

• Strategic familiarity with manufacturing, autonomous vehicles, or healthcare domains and the business and technical requirements that shape AI infrastructure in those industries.

• Demonstrated ability to influence robotics, manufacturing, or autonomous systems teams and shape architectural decisions that bridge domainexpertisewith modern AI/ML capabilities.

• Track recordof translating specific domain engagements into generalizable, org-wide AI infrastructure capabilities and standards.