About the Role
We are seeking a Kubernetes AI Workload Engineer to design, build, and support containerized AI workloads with reliable scaling, scheduling, and resource management. The ideal candidate will collaborate with business, data, and engineering teams to deliver secure, scalable, and measurable AI infrastructure capable of running machine learning and Generative AI applications in production.
Key Responsibilities
- Design, deploy, and manage containerized AI and machine learning workloads on Kubernetes.
- Build scalable AI infrastructure with efficient scheduling, resource allocation, and workload orchestration.
- Package and deploy AI applications using Helm and Docker.
- Optimize GPU utilization for AI training and inference workloads.
- Configure autoscaling mechanisms to improve performance, availability, and cost efficiency.
- Implement observability solutions for monitoring cluster health, application performance, and resource utilization.
- Collaborate with AI engineers, data scientists, DevOps teams, and business stakeholders to deliver production-ready AI platforms.
- Troubleshoot Kubernetes clusters, AI workloads, networking, and deployment issues.
- Implement security, governance, and operational best practices for AI infrastructure.
- Maintain infrastructure documentation, deployment standards, and automation workflows.
Required Skills
- Strong experience with Kubernetes
- Hands-on experience with Helm and Docker
- Knowledge of GPU-enabled AI workloads
- Experience implementing autoscaling strategies
- Familiarity with observability, monitoring, and logging tools
- Understanding of container orchestration, scheduling, and resource management
- Experience with Linux and infrastructure automation
Experience Requirements
- Up to 5 years of overall experience
- Minimum 1–2 years of relevant hands-on experience in Kubernetes, container platforms, AI infrastructure, DevOps, or related technologies
