You Are
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. You bring extensive experience as a lead and senior architect, command over a broad landscape of technological options, and the latest innovations that can be introduced into solutions. You have a proven track record of designing and deploying large‑scale infrastructures that deliver business value, move the needle for the organizations they serve, and tailor architectures to each client’s situation, standards, and strategic objectives.
You are a recognized expert across at least one hyperscaler cloud, possess deep, current knowledge of AI/ML services, accelerators, interconnects, and cost levers, and continuously scout emerging technologies. You maintain strong relationships with infrastructure partners and represent the practice’s technical credibility in forums. Beyond architecture, you shape strategy, roadmaps, establish standards, mentor the architect community, and ensure the firm delivers AI/ML infrastructure that meets business SLAs, controls cost, scales to frontier workloads, and creates lasting business value.
Responsibilities
- Set the overarching technical vision and strategy for compute infrastructure supporting large‑scale AI/ML systems.
- Own the most complex, high‑stakes architecture decisions across compute, networking, storage, orchestration, and model serving, rationalizing options and making authoritative choices aligned to client situations, standards, and strategic objectives.
- Architect and 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 foundational tooling, infrastructure‑as‑code, and automation.
- Lead enterprise‑scale architecture assessments and design reviews, validating findings, profiling real workloads, and demonstrating 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 pilot promising emerging technologies and innovations, building and testing them in real conditions to judge fit in solutions.
- Drive 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, applying hands‑on expertise in AI/ML services, accelerators, interconnects, and cost levers with 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 problems others cannot and codifying fixes.
- Cultivate and lead relationships with infrastructure partners and partnering organizations, securing early access, influence, and insight, and representing practice credibility.
- 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 InfraOps and MLOps, implementing 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 through hands‑on pairing, design collaboration, and code/architecture reviews.
- Champion cost‑efficiency and value realization, ensuring infrastructure moves the needle for the organizations it serves.
Education
- Bachelor’s Degree in Computer Science, Computer Engineering, or a related engineering field.
Qualifications
- Significant experience in coding, building, monitoring, troubleshooting applications of AI/ML models and designing 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, preferably 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, managing 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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