AI Infrastructure Principal Architect

Overview

As a Principal AI Infrastructure Architect, you define the technical direction for large-scale AI/ML compute ecosystems, balancing diverse architectures across compute, networking, and model serving. You shape strategy, standards, and tooling, driving cost-aware, scalable solutions that deliver measurable business value. You work closely with cross-functional teams and partners to validate designs, prototype reference implementations, and guide engagements toward production-ready outcomes. This role centers on strategic leadership, hands-on engineering, and influential technical stewardship.

Responsibilities

• Set and advance the overarching compute infrastructure vision for AI/ML systems
• Make authoritative architecture decisions across compute, networking, storage, orchestration, and model serving
• Architect and prototype cost-optimized, large-scale training systems with reference implementations and benchmarks
• Define reference architectures, standards, and patterns and implement foundational tooling and automation
• Lead enterprise-scale architecture reviews and validate findings through hands-on environment work
• Shape the AI infrastructure roadmap, capacity planning, and technology evolution
• Pilot emerging technologies and assess their fit in real solutions
• Optimize performance and cost of GPU/compute workloads and ensure SLAs
• Act as principal authority across hyperscaler platforms with hands-on AI/ML expertise
• Troubleshoot complex stack issues and codify fixes across hardware, software, and models
• Foster relationships with infrastructure partners and represent technical credibility
• Provide executive and client advisory translating trade-offs into business outcomes
• Define monitoring, observability, reliability strategy, and governance for production AI/ML systems
• Ensure security, compliance, and regulatory alignment of AI infrastructure
• Mentor and develop the architect community and drive cross-team collaboration
• Champion cost-efficiency and value realization in scalable infrastructure

Key requirements

• Significant experience in coding, building, monitoring, and troubleshooting AI/ML model applications and deploying them on premises or in the cloud
• Strong understanding of AI/ML concepts and computing infrastructure
• Proven programming experience in Python, Java, or C++
• Experience with data pipelines/workflow tools (e.g., Apache Airflow, Kubeflow)
• Strong problem-solving skills and ability to work in fast-paced environments
• Excellent communication and collaboration skills
• Extensive experience in AI/ML infrastructure engineering or related roles on hyperscale platforms
• Proven track record leading AI projects and teams
• Strong project management and multi-project execution能力
• Experience evaluating/selecting AI technologies and frameworks
• Ability to drive alignment across cross-functional teams

• strong communication
• leadership and mentoring
• cross-functional collaboration
• Python
• Java
• C++

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