AI Infrastructure Architect

Overview

In this hands-on role, you design and optimize AI/ML infrastructure that powers real-world applications. You work with senior architects, lead your own workstreams, and mentor junior engineers to raise the bar for reliability and performance. You will architect GPU clusters, distributed training environments, and end-to-end deployment pipelines, turning complex requirements into scalable, secure systems. You own production readiness across InfraOps and MLOps, driving improvements in monitoring, cost, and scalability. This is a ownership-driven opportunity to shape the infrastructure stack that enables AI-driven business outcomes.

Responsibilities

• Write, review, and debug code, scripts, and infrastructure-as-code for AI infrastructure and tooling
• Architect, configure, and provision compute resources across cloud and on-premises environments (GPU clusters, distributed training)
• Design and maintain deployment automation and CI/CD pipelines for AI systems and models
• Deploy AI systems, models, and data pipelines into production with best practices
• Lead container orchestration and model serving (Docker, Kubernetes, model deployment frameworks)
• Optimize the computational stack for performance, power, cost, and scalability
• Evaluate and select tools and platforms to shape the infrastructure roadmap
• Integrate AI models into existing enterprise systems with security and compliance
• Own AI monitoring and infrastructure health across InfraOps and MLOps
• Troubleshoot complex issues across hardware, networking, software, and models; lead root-cause analysis
• Mentor junior engineers and lead code reviews
• Define and document architecture standards, processes, and security and cost-efficiency practices

Key requirements

• Practical experience coding, building, monitoring, and troubleshooting AI/ML applications and infrastructure
• Strong understanding of AI and machine learning concepts
• Strong understanding of computing infrastructure; knowledge of AI infrastructure preferred
• Proficiency in 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 a fast-paced environment
• Excellent communication and collaboration skills
• Proven experience in AI/ML infrastructure engineering on a hyperscaler platform for deploying large-scale solutions

• strong communication
• collaboration
• mentoring
• Python
• Java
• C++

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