![How 6 Leading Brands Use Content To Win Audiences [E-Book]](https://contentmarketinginstitute.com/wp-content/uploads/2025/03/content-marketing-examples-600x330.png?#)
![Building A Digital PR Strategy: 10 Essential Steps for Beginners [With Examples]](https://buzzsumo.com/wp-content/uploads/2023/09/Building-A-Digital-PR-Strategy-10-Essential-Steps-for-Beginners-With-Examples-bblog-masthead.jpg)
![How to Use GA4 to Track Social Media Traffic: 6 Questions, Answers and Insights [VIDEO]](https://www.orbitmedia.com/wp-content/uploads/2023/06/ab-testing.png)
Platform Engineer for AI Systems
A company is looking for a Platform Engineer (DevOps for Applied AI Systems). Key Responsibilities Design and implement scalable AI model inference environments, optimizing GPU utilization and autoscaling strategies Develop and maintain infrastructure as code (IaC) using tools like Terraform, Pulumi, or CloudFormation Monitor system performance and implement failover mechanisms for model-serving reliability Required Qualifications 5-8+ years of experience in DevOps, Platform Engineering, or Cloud Infrastructure with a focus on AI/ML workloads 3+ years of experience with deploying and scaling AI models in production Expertise in containerization and orchestration tools (Docker, Kubernetes, Nomad) Strong knowledge of cloud platforms (AWS, GCP, Azure) and GPU-based model deployments Experience with CI/CD pipelines and infrastructure automation
A company is looking for a Platform Engineer (DevOps for Applied AI Systems).
Key Responsibilities
Design and implement scalable AI model inference environments, optimizing GPU utilization and autoscaling strategies
Develop and maintain infrastructure as code (IaC) using tools like Terraform, Pulumi, or CloudFormation
Monitor system performance and implement failover mechanisms for model-serving reliability
Required Qualifications
5-8+ years of experience in DevOps, Platform Engineering, or Cloud Infrastructure with a focus on AI/ML workloads
3+ years of experience with deploying and scaling AI models in production
Expertise in containerization and orchestration tools (Docker, Kubernetes, Nomad)
Strong knowledge of cloud platforms (AWS, GCP, Azure) and GPU-based model deployments
Experience with CI/CD pipelines and infrastructure automation