Architect and deliver AI-native, end-to-end system debug solutions for hyperscale datacenter infrastructure, combining engineering expertise, telemetry, automation, and agentic workflows. Design and orchestrate human-agent engineering workflows that accelerate failure detection, triage, segmentation, root cause analysis, mitigation, remediation, and learning. Build specialized engineering agentic skills, tools, and reusable workflows that codify expert debug knowledge and enable autonomous or human-supervised execution at scale. Lead cross-functional debug and root-cause initiatives spanning hardware, firmware, software, platform, and datacenter systems, using agents and automation to reduce coordination and investigation overhead. Own measurable improvements in system reliability and debug effectiveness, including faster fault isolation, reduced time to root cause, defect reduction, automation coverage, and prevention of repeat failures. Lead investigation of complex and ambiguous system failures, applying deep technical judgment where automated or agentic systems encounter novel scenarios, insufficient evidence, or conflicting signals. Develop world-class at-scale debug methodologies, diagnostic strategies, validation techniques, and automated test routines for datacenter systems. Engineer closed-loop learning systems that convert incidents, debug evidence, root causes, and engineering decisions into reusable knowledge that continuously improves agents, automation, diagnostics, and future platform readiness. Define safe and governed agentic execution models, including human-in-the-loop controls, validation, observability, permissions, and escalation paths for mission-critical engineering workflows. Drive the transition from engineer-driven execution to outcome-driven engineering, where engineers define intent and guardrails while agents perform appropriate investigation, correlation, orchestration, and repetitive execution. Provide technical leadership on emerging AI, agent, diagnostic, telemetry, and automation technologies, identifying opportunities to fundamentally improve how Fleet Engineering operates at datacenter scale. Communicate complex engineering insights, decisions, risks, and progress through data-driven narratives that enable effective collaboration and decision-making across engineering teams, partners, and leadership. 1. Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Expertise in at least one layer of server or datacenter infrastructure (CPU or SoC architecture, memory subsystem, storage, networking, virtualization, firmware, cloud-native operating systems, or fleet telemetry and data systems), with working fluency in how failures propagate. These requirements include, but are not limited to, the following specialized security screening: Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python Hands-on experience with AI agents in production operations: setting intent and success criteria, evaluating output against outcomes, curating the memory they rely on, governing what each may do, and solving the problems they have not seen. Analytical rigor with large-scale telemetry and data tools: defining each metric once (population, start and end events, denominator), separating measured results from targets, proxies and estimates, and counting distinct machines, not retries. Experience leading without line authority across organizational boundaries: aligning partners on workflow ownership, interfaces and decision rights, and adopting ideas from other teams on evidence. Decision-ready communication for engineers and executives in writing, visuals and live forums, including technical reports that state the conclusion, the evidence, the confidence and what would change the answer. Planning discipline in a fast-changing environment: works in small, reversible steps, adapts as evidence changes, manages time and priorities, and sets a pace of change the team can absorb.