AI Infrastructure Architect - Infrastructure & Capital Projects Engineering (All Genders)
Remote • Kronberg, Campus Kronberg 1
Posted 3d ago
Job Location
Kronberg, Campus Kronberg 1
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Infrastructure Engineer
About the job
As a hands-on AI Infrastructure Architect, you will be responsible for designing and optimizing the AI and machine learning infrastructure that powers real-world applications. You will work alongside senior architects and engineers, applying your skills in coding, testing, configuring, deploying, monitoring, and troubleshooting AI systems and their underlying infrastructure. This role involves architecting and optimizing infrastructure components, writing and reviewing code and deployment scripts, designing and tuning compute resources like GPU clusters and distributed training environments, and deploying AI systems into production. You will also build and optimize data pipelines, monitor system health across InfraOps and MLOps, and troubleshoot complex issues. Additionally, you will mentor junior engineers, contribute to architectural decisions, and help establish best practices, making meaningful contributions to infrastructure that enables AI-driven business outcomes.
Responsibilities
- Write, review, and debug code, scripts, and infrastructure-as-code for AI infrastructure, automation, and tooling.
- Architect, configure, and provision compute resources across cloud and on-premises environments, including GPU clusters and distributed training setups.
- Design and maintain deployment automation and CI/CD pipelines for AI systems, models, and applications.
- Deploy AI systems, models, and data pipelines into production.
- Lead container orchestration and model serving using tools such as Docker and Kubernetes.
- Architect and optimize the computational stack for performance, power, cost, and scalability.
- Evaluate and select tools, frameworks, and platforms for the infrastructure roadmap.
- Integrate AI models and systems into existing enterprise systems.
- Own AI monitoring and infrastructure health across InfraOps and MLOps.
- Troubleshoot and resolve complex issues across the computational stack.
- Mentor junior engineers and lead code reviews.
- Define and document architecture standards, processes, and procedures, applying best practices for security, cost-efficiency, and scalability.
Requirements
- Practical experience in coding, building, monitoring, and troubleshooting AI/ML models and their infrastructure.
- Strong understanding of AI and machine learning principles.
- Strong understanding of computing infrastructure, with preferred knowledge of AI infrastructure.
- Proficiency in programming languages such as Python, Java, or C++.
- Experience with data pipeline and workflow management 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 or related roles on a hyperscaler platform for deploying large-scale solutions.
- Bachelor's Degree in Computer Science, Computer Engineering, or a related Engineering field.