AI Infrastructure Principal Architect

Overview

You lead the technical direction for compute infrastructure powering large-scale AI/ML systems. You own high-stakes architectural decisions across compute, networking, storage, orchestration, and model serving, balancing multiple viable options. You prototype cost-optimized, scalable reference implementations and drive standards, tooling, and automation. You influence partnerships and provide executive-level technical advisory tied to business outcomes. You champion cost-efficiency, reliability, and measurable value in production AI/ML workloads.

Responsibilities

• Set the overarching technical vision for compute infrastructure enabling large-scale AI/ML systems.
• Make authoritative architectural choices across compute, networking, storage, orchestration, and model serving.
• Architect and prototype large-scale, cost-optimized systems with proofs-of-concept and benchmarks.
• Define reference architectures, standards, and patterns with foundational tooling and automation.
• Lead enterprise-scale architecture assessments and design reviews with hands-on validation.
• Shape the AI infrastructure roadmap, capacity planning, and technology evolution.
• Identify and pilot emerging technologies with real-condition testing.
• Drive performance and cost optimization of GPU/compute workloads, tuning distributed training and model serving.
• Act as the principal authority across hyperscaler cloud platforms with hands-on AI/ML expertise.
• Lead troubleshooting and root-cause analysis across hardware, networking, software, and models.
• Cultivate relationships with infrastructure partners and represent technical credibility in forums.
• Provide executive- and client-level advisory translating trade-offs into business outcomes.
• Define monitoring, observability, and reliability strategy for InfraOps and MLOps with governance.
• Ensure enterprise integration, security, and regulatory alignment of AI/ML infrastructure.
• Mentor and develop the architect community, conducting pairing, collaboration, and reviews.
• Champion cost-efficiency and value realization in infrastructure.

Key requirements

• Significant experience coding, building, monitoring, and troubleshooting AI/ML applications on premises or in the cloud.
• Strong understanding of AI and machine learning concepts.
• Deep knowledge of computing infrastructure and AI infrastructure specifics.
• Proficiency in Python, Java, or C++.
• Experience with data pipelines and workflow tools (e.g., Apache Airflow, Kubeflow).
• Excellent problem-solving skills in fast-paced environments.
• Strong communication and collaboration abilities.
• Extensive experience in AI/ML infrastructure engineering or similar roles on hyperscaler platforms.
• Proven leadership and management of AI projects and teams.
• Strong project management skills and ability to manage multiple projects.
• Experience evaluating and selecting AI technologies and frameworks.
• Ability to work with cross-functional teams and drive alignment.

• strong communication
• leadership
• collaboration
• AI/ML infrastructure engineering
• hyperscaler cloud platforms
• distributed training systems

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