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# Lead Platform Engineer/Architect - HPC, Kubernetes

- **Company:** EPAM Systems, Inc.
- **Location:** Los Angeles, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $145,000.0 - $170,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, C++ (Programming Language), Software Debugging, Job Scheduling, Python (Programming Language), Reliability Engineering, Azure Machine Learning, High Performance Computing, Multi-Cloud, Kubernetes, Terraform, Oracle Cloud Infrastructure
- **Published:** October 9, 2026
- **Apply:** https://www.beyondlosangeles.com/job.asp?id=3426305989&tx=FK8986FFR&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* 10+ years of experience in infrastructure engineering, cloud platforms, or high-performance computing environments
 * Expert-level Kubernetes experience at meaningful scale, including node pool sizing, scheduler debugging, CNI troubleshooting, and rolling upgrades across large fleets
 * Advanced Python skills with a track record of building production-grade tools, not just scripts; experience with Go, Rust, or C++ is a strong plus
 * Daily proficiency in Terraform for writing and reviewing infrastructure as code
 * Working knowledge of core AWS services including EC2, S3, EFS, and FSx for Lustre
 * Strong site reliability engineering background with experience building monitoring, alerting, and incident response practices
 * CKA, CKS certificates are highly preferred

## Description

* Operate and scale Kubernetes platforms (EKS, GKE, and other distributions) including cluster lifecycle management, node pool optimization, and networking policies during periods of rapid growth
 * Provision and manage HPC infrastructure through CI/CD pipelines spanning AWS, CoreWeave, GCP, OCI, and additional cloud providers
 * Design and maintain job scheduling systems that efficiently allocate GPU compute resources across training and inference workloads
 * Define SLIs/SLOs, build robust monitoring and alerting systems, and actively participate in incident response and post-incident reviews
 * Develop production-quality tooling and automation to support multi-cloud infrastructure operations at scale
 * Collaborate daily with Networking, Storage, Security, and AI/ML platform teams to ensure seamless cross-functional infrastructure delivery