AI Infrastructure Principal Architect

Self Mandate

As a Principal AI Infrastructure Architect, you are the firm's most senior technical authority on compute infrastructure for large‑scale AI and machine learning systems, bringing extensive experience as a lead and senior architect along with command over a broad landscape of technological options and the latest innovations that can be introduced into a solution.

You have a proven track record of successfully designing and deploying large‑scale infrastructure on which significant AI/ML solutions operate in production and demonstrably deliver business value — not just systems that perform, but systems that move the needle for the organizations they serve.

You weigh and rationalize multiple viable architectures across compute, networking, storage, orchestration, and model serving, making authoritative decisions tailored to each client's situation, standards, and strategic objectives, and you set the technical direction that senior and lead architects build upon.

As a recognized expert across at least one hyperscaler cloud — and conversant across several — you bring deep, current knowledge of AI/ML services, accelerators, interconnects, and cost levers, and you continuously scan the horizon to identify promising emerging technologies and judge where and when they belong in a real solution.

You are well known to our infrastructure partners and partnering organizations, maintaining strong relationships that give the firm early access, influence, and insight, and you represent the practice's technical credibility in those forums.

Beyond architecture, you shape strategy and roadmaps, establish standards and reference architectures, mentor and elevate the architect community, and are ultimately accountable for ensuring the firm delivers AI/ML infrastructure that meets business SLAs, controls cost, scales to frontier workloads, and creates lasting business value.

• The Work
• Set the overarching technical vision and strategy for compute infrastructure supporting large‑scale AI/ML systems, establishing the direction that senior and lead architects build upon.
• Own the most complex, high‑stakes architecture decisions across compute, networking, storage, orchestration, and model serving, rationalizing multiple viable options and making authoritative choices aligned to client situations, standards, and strategic objectives.
• Architect and hands‑on prototype large‑scale, cost‑optimized compute and distributed training systems — building reference implementations, proofs‑of‑concept, and benchmarks to validate designs before they scale.
• Define reference architectures, standards, and architectural patterns that scale across engagements, and personally implement the foundational tooling, infrastructure‑as‑code, and automation that anchor them.
• Lead enterprise‑scale architecture assessments and design reviews, getting hands‑on in the environment to validate findings, profile real workloads, and demonstrate optimization opportunities.
• Shape and steward the AI infrastructure roadmap and technology strategy, planning capacity, scaling, and technology evolution in step with long‑term business goals.
• Identify, evaluate, and hands‑on pilot promising emerging technologies and innovations, building and testing them in real conditions to judge where and when they belong in a solution.
• Drive hands‑on performance and cost optimization of the computational stack, profiling GPU/compute utilization, tuning distributed training and model‑serving workloads, and engineering improvements to meet SLAs while controlling cost.
• Serve as the principal authority across hyperscaler cloud platforms, with current, hands‑on expertise in AI/ML services, accelerators, interconnects, and cost levers, and breadth across multiple providers.
• Lead deep, hands‑on troubleshooting and root‑cause analysis of the most complex issues across the stack — hardware, networking, software, and models — resolving the problems others cannot and codifying the fixes.
• Cultivate and lead relationships with infrastructure partners and partnering organizations, securing early access, influence, and insight, and representing the practice’s technical credibility in those forums.
• Provide executive‑ and client‑level technical advisory, translating complex infrastructure trade‑offs into clear, defensible recommendations tied to business outcomes.
• Define monitoring, observability, and reliability strategy across Infra Ops and MLOps, and implement the instrumentation, SLAs, SLOs, and cost/performance governance for production AI/ML systems.
• Ensure enterprise integration, security, compliance, and regulatory alignment of AI/ML infrastructure across the firm’s solutions.
• Mentor, elevate, and grow the architect community, developing senior and lead architects through hands‑on pairing, design collaboration, and code/architecture reviews.
• Champion cost‑efficiency and value realization, ensuring infrastructure not only performs and scales but demonstrably moves the needle for the organizations it serves.

Education

• Bachelor’s Degree in Computer Science, Computer Engineering, or related Engineering field.
• Basic (required) Qualification
• Significant experience in coding, building, monitoring, troubleshooting applications of AI/ML models; selecting, designing and infrastructure for deploying and running them on premise or on public cloud.
• Strong understanding of AI and machine learning as a subject.
• Strong understanding of computing infrastructure a subject, preferred knowledge of AI infrastructure.
• Well versed and proven experience in programming languages such as Python, Java, or C++.
• Experience with data pipeline and workflow management tools (e. g., Apache Airflow, Kubeflow).
• Strong problem‑solving skills and ability to work in a fast‑paced environment.
• Excellent communication and collaboration skills.
• Longstanding experience in AI/ML infrastructure engineering or related roles on a hyperscaler platform for deploying large scale solutions.
• Proven experience in leading and managing AI projects and teams.
• Strong project management skills, with the ability to manage multiple projects simultaneously.
• Demonstrated experience in evaluating and selecting AI technologies and frameworks.
• Ability to work with cross‑functional teams and drive project alignment.
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