United States onlyThis position is posted by Jobgether on behalf of a partner company. We are currently looking for an Infrastructure Engineer - Kubernetes, AI and GPU in the United States.
This role offers the opportunity to architect and manage high-performance on-prem cloud infrastructure and GPU rendering platforms for complex, resource-intensive workloads. You will design, deploy, and optimize Kubernetes clusters, automate provisioning and CI/CD pipelines, and maintain both Linux and Windows environments. Collaborating closely with engineering, creative, and security teams, you will ensure the reliability, scalability, and performance of infrastructure that supports AI/ML workloads, high-performance rendering, and other compute-intensive processes. This position combines hands-on infrastructure management with opportunities to influence and improve production workflows.
Accountabilities:
- Design, deploy, and maintain on-prem cloud and virtualization platforms, including Kubernetes and OpenNebula.
- Build, optimize, and manage containerized workloads, particularly for GPU-intensive applications.
- Automate provisioning, configuration, and deployment processes using tools such as Ansible, Terraform, and Packer.
- Configure and manage physical infrastructure including servers, storage, and networking equipment.
- Monitor system performance and health using Prometheus, Grafana, and ELK; maintain backup, restore, and disaster recovery processes.
- Support infrastructure, engineering, and creative teams in production environments.
- Document systems, processes, and runbooks, and provide technical troubleshooting support.
- Ensure secure data handling practices, compliance, and adherence to relevant standards (MPAA, NIST, ISO).
Requirements
- 3+ years of experience in infrastructure engineering or DevOps roles.
- Strong Linux and Windows administration across multiple distributions.
- Hands-on experience with Kubernetes: cluster setup, configuration, management, and optimization.
- Proficiency in infrastructure-as-code tools such as Terraform, Ansible, and Packer.
- Experience with physical infrastructure setup including servers, storage arrays, and networking equipment.
- Familiarity with software-defined networking and Kubernetes networking stacks.
- Proficiency in scripting languages (Python, Bash).
- Understanding of CI/CD principles and pipeline design.
- Comfortable working in fast-paced, collaborative, and creative environments.
- Bonus skills: GPU orchestration, AI/ML pipelines, containerized GUI applications, VFX or scientific computing backgrounds, familiarity with creative tools such as Nuke, Houdini, Maya.
Benefits
- Competitive annual salary range: $130,000–$145,000 DOE.
· Remote work flexibility.
- Opportunity to work on cutting-edge GPU and AI/ML infrastructure projects.
- Exposure to cloud-native architecture and high-performance computing environments.
- Collaborative and creative team environment.
- Professional development opportunities and skill growth in infrastructure engineering and AI/ML workflows.
Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.
When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly.
🔍 Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements.
📊 It compares your profile to the job’s core requirements and past success factors to determine your match score.
🎯 Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role.
🧠 When necessary, our human team may perform an additional manual review to ensure no strong profile is missed.
The process is transparent, skills-based, and free of bias, focusing solely on your fit for the role.
Once the shortlist is completed, it is shared directly with the company that owns the job opening. The final decision and next steps (such as interviews or assessments) are then managed by their internal hiring team.
