AI Infrastructure Architect

Job summary

Designs, deploys, and optimizes infrastructure for AI and machine-learning systems across cloud and on-premises environments, including GPU clusters and distributed training. Builds infrastructure-as-code, CI/CD pipelines, production data workflows, and model-serving platforms; monitors system performance and reliability across InfraOps and MLOps; and troubleshoots issues across hardware, networking, software, and models. The role also mentors engineers and establishes architecture, security, cost-efficiency, and scalability practices.

Qualifications

• Practical experience coding, building, monitoring, and troubleshooting AI/ML model applications.
• Experience selecting and designing infrastructure to deploy and run AI/ML systems on-premises or in public cloud.
• Strong understanding of artificial intelligence and machine learning.
• Strong understanding of computing infrastructure; AI infrastructure knowledge is preferred.
• Proficiency in Python, Java, or C++.
• Experience with data pipeline and workflow management tools such as Apache Airflow or Kubeflow.
• Proven AI/ML infrastructure engineering or related experience deploying large-scale solutions on a hyperscaler platform.
• Bachelor's degree in Computer Science, Computer Engineering, or a related engineering field.

Responsibilities

• Write, review, and debug code, scripts, and infrastructure-as-code for AI infrastructure and automation.
• Architect and provision cloud and on-premises compute, including GPU clusters and distributed training environments.
• Design and maintain deployment automation and CI/CD pipelines for reliable releases.
• Deploy AI systems, models, and data pipelines into production.
• Lead container orchestration and model serving using Docker, Kubernetes, and model deployment frameworks.
• Optimize computational infrastructure for performance, power, cost, utilization, and scalability.
• Evaluate infrastructure tools, frameworks, and platforms and recommend roadmap choices.
• Integrate AI systems with enterprise platforms while supporting interoperability, security, and regulatory compliance.
• Monitor AI and infrastructure performance, reliability, and utilization across InfraOps and MLOps.
• Troubleshoot complex hardware, networking, software, and model issues and lead root-cause analysis.
• Mentor junior engineers, lead code reviews, and provide technical direction.
• Document architecture standards and processes and apply security, cost-efficiency, and scalability practices.

Skills

Degrees

Languages

Relocation