AI Infrastructure Junior Architect

YOU ARE 

As a hands on Infrastructure architect, you are an early-career engineer who learns and grows while contributing hands-on to the AI and machine learning infrastructure that powers real-world applications.  Under the guidance of senior architects and engineers, you'll develop practical skills in coding, testing, configuring, deploying, monitoring, and troubleshooting AI systems and the infrastructure they run on. Day to day, you'll write and test code and deployment scripts, help configure cloud and on-premises compute resources such as GPU clusters and distributed training environments, deploy AI systems and models into production, and support data pipelines that feed AI and ML workflows. You'll learn to monitor AI systems and infrastructure health across both InfraOps and MLOps disciplines, perform AI monitoring to track model and system performance, and troubleshoot issues across the computational stack with mentorship and support. This is a hands-on, learning-focused role where you build expertise across modern tools and platforms — including container orchestration, model serving, CI/CD pipelines, InfraOps, MLOps, and AI monitoring — while making meaningful contributions to infrastructure that enables AI-driven business outcomes. 

THE WORK 

• Write, test, and debug code and scripts for AI infrastructure tasks, including automation and tooling, under the guidance of senior engineers. 

• Develop and maintain infrastructure and software deployment scripts to support reliable, repeatable releases of AI systems and models. 

• Configure and provision compute resources across cloud and on-premises environments, including GPU clusters and distributed training setups. 

• Deploy AI systems and machine learning models into production infrastructure, following established processes and best practices. 

• Deploy data pipelines that feed AI and ML workflows, ensuring data is available, clean, and reliable. 

• Assist with container orchestration and model serving, learning tools such as Docker, Kubernetes, and model deployment frameworks. 

• Support and maintain CI/CD pipelines for automating the build, test, and deployment of AI infrastructure and applications. 

• Monitor AI systems and infrastructure health across InfraOps and MLOps disciplines, tracking performance, reliability, and resource utilization. 

• Perform AI monitoring to track model performance, detect drift or degradation, and surface issues for review. 

• Troubleshoot and help resolve issues across the computational stack — hardware, networking, software, and models — with mentorship and support. 

• Document configurations, processes, and procedures to maintain clear, repeatable, and shareable knowledge across the team. 

• Collaborate with senior architects and engineers, participating in code reviews, team discussions, and knowledge-sharing sessions to grow technical skills. 

• Apply security, cost-efficiency, and scalability best practices as you learn them, contributing to well-managed and responsible infrastructure.