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 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.
