Senior AI Infrastructure Engineer - EDA Infrastructure

About This Role

Supporting the operation of the company's GPU cloud services is the core of this role for an AI infrastructure engineer. You will build the systems, tooling, and data infrastructure that enable engineering teams to innovate while proactively identifying, tracking, and mitigating risks across the entire technical task. This role suits engineers who thrive at the intersection of product, infrastructure, and software engineering and want to build automated, intelligence-driven systems that protect the company's most critical AI platforms.

Responsibilities

• Build and operate scalable telemetry pipelines for metrics, logs, traces, and events across on-premise, CSP, and NCP clusters
• Establish common instrumentation, collection, storage, and access patterns so teams can generate and consume telemetry consistently
• Deliver dashboards, alerting, and analysis capabilities that improve service visibility, detection, and troubleshooting
• Standardize and automate incident, maintenance, service on-call, and support on-call workflows across HWInf
• Integrate operational data and lifecycle signals to improve ownership, escalation, communication, and post-incident learning
• Build reporting and AI-assisted tooling that reduces manual toil and improves operational responsiveness
• Build and maintain physical hardware and software catalogs as trusted sources of truth for infrastructure inventory, service ownership, dependencies, and documentation
• Create consistent data models and integration pipelines that connect clusters, hardware, services, teams, and operational workflows
• Provide self-service discovery capabilities so engineers can quickly identify what they operate, who owns it, and how to support it

Requirements

• BS degree in Computer Science, Computer Engineering, or a related technical field, or equivalent experience
• 8+ years of experience in infrastructure security, platform engineering, or security tooling
• Proficiency in one or more programming languages such as Python, Go, Typescript, or Java
• Strong understanding of software and infrastructure principles, with experience applying them in production environments
• Ability to lead cross-functional initiatives that span internal teams and external partners in varying disciplines across engineering, product, finance, and security

Nice to have:

• Experience building and operating incident-management processes with internally built and/or externally vended SaaS tools
• Experience working with building, deploying, and maintaining ML models in production systems along with familiarity with AI agent frameworks or orchestration tools
• Experience building and operating modern observability platforms to deliver scalable metrics, logs, traces, and profiling