AI Infrastructure Lead Architect

Overview

In this role you lead the end-to-end design of optimized compute infrastructure for large-scale AI/ML systems, balancing performance, cost, and scalability. You translate business goals and SLAs into robust architectures across compute, networking, storage, and orchestration. You shape roadmaps, guide engineering teams, and ensure security, compliance, and production readiness. This is a mission-driven opportunity to set standards for enterprise AI infrastructure on leading hyperscaler platforms.

Responsibilities

• Own end-to-end architecture and design of optimized compute infrastructure for large-scale AI/ML systems, from concept to delivery
• Evaluate architecture alternatives across compute, networking, storage, orchestration, and model serving to fit client constraints
• Lead architecture assessments, identify gaps and bottlenecks, and recommend remediation
• Drive architectural decision-making with transparent trade-offs aligned to SLAs and standards
• Define AI infrastructure roadmap including capacity planning, scaling, and tech evolution
• Design and optimize the full computational stack for performance, power, cost, and scalability
• Design and tune large-scale GPU clusters and distributed training systems, including interconnects and storage
• Serve as authoritative AI infra expert on at least one hyperscaler cloud (AWS/Azure/GCP)
• Design deployment, automation, and CI/CD strategies for reliable releases of AI systems and data pipelines
• Establish AI monitoring and observability strategy across InfraOps and MLOps with SLAs, SLOs, alerts, and cost tracking
• Integrate AI/ML systems into enterprise environments ensuring interoperability, security, and regulatory compliance
• Lead capacity planning and cost modeling to forecast compute needs while maintaining performance
• Collaborate with clients and teams to translate requirements into architecture and standards
• Set technical direction, standards, and best practices, mentoring engineers and leading reviews

Key requirements

• Solid background in coding, building, monitoring, troubleshooting AI/ML model applications and deploying them on premises or public cloud
• Strong understanding of AI and machine learning concepts
• Strong understanding of computing infrastructure; preferred knowledge of AI infrastructure
• Proficiency in programming languages such as Python, Java, or C++
• Experience with data pipelines and workflow tools (e.g., Apache Airflow, Kubeflow)
• Strong problem-solving skills and ability to work in fast-paced environments
• Excellent communication and collaboration skills
• Significant experience in AI/ML infrastructure engineering on a hyperscaler platform for large-scale deployments
• Proven leadership and people-management experience on AI projects and teams
• Strong project management skills with ability to manage multiple projects
• Demonstrated experience evaluating and selecting AI technologies and frameworks
• Ability to work with cross-functional teams and drive project alignment

• Communication
• Collaboration
• Leadership
• GPU cluster design and tuning
• Distributed training systems
• Hyperscaler AI/ML services, accelerators, networking, cost levers

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