EKS Cluster & Memory Profiling Engineer

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EKS Cluster & Memory Profiling Engineer

Location: Seattle, WA
Client: Amazon
Job Type: Contract
Experience: 5+ Years

Job Description

We are seeking an experienced EKS Cluster & Memory Profiling Engineer with strong hands-on experience in Amazon EKS, Kubernetes, AWS infrastructure, memory profiling, performance analysis, and resource optimization.

The engineer will be responsible for end-to-end operations and optimization of assigned EKS services, with a focus on container and pod-level memory utilization, resource right-sizing, node-pool optimization, and production performance.

Responsibilities

• Perform end-to-end operations and optimization of Amazon EKS clusters and Kubernetes workloads.

• Conduct container and pod-level memory profiling and performance analysis.

• Analyze memory utilization, memory pressure, OOM events, and resource consumption.

• Right-size Kubernetes CPU and memory requests and limits based on production workload behavior.

• Implement and optimize Vertical Pod Autoscaler (VPA) recommendations.

• Optimize EKS node pools and workload placement for improved resource utilization.

• Troubleshoot Linux cgroups, container memory usage, OOMKilled events, and resource contention.

• Evaluate EC2 instance-family selection and right-sizing strategies.

• Identify opportunities for Graviton/ARM64 adoption and infrastructure optimization.

• Deploy production changes while maintaining performance, throughput, availability, and SLA requirements.

• Perform before-and-after performance analysis to validate optimization results.

• Develop technical documentation including runbooks, SOPs, and design documents.

• Support large-scale infrastructure optimization and cost-reduction initiatives.

Basic Qualifications

• 5+ years of hands-on production engineering experience in at least one of the following ecosystems: Rust, EKS, JDK/JVM, EMR/Spark SQL, or EMR/Spark infrastructure.

• Strong experience with Amazon EKS and Kubernetes in production environments.

• Experience with memory profiling, performance analysis, and resource optimization in large-scale distributed systems.

• Experience with Kubernetes resource requests, limits, workload optimization, and capacity management.

• Strong understanding of Linux memory management, containers, and cgroups.

• Proven experience deploying production changes without degradation to performance, throughput, or availability SLAs.

• Experience creating technical documentation for engineering audiences, including runbooks, SOPs, and design documentation.

Preferred Qualifications

• Experience with Amazon internal tooling and deployment mechanisms such as MCM, CDK, CargoBrazil, and Smithy.

• Familiarity with Graviton/ARM64 architectures and instance-family right-sizing strategies.

• Experience with AI coding agents or automated workflow tooling.

• Experience with fleet-scale optimization programs or large-scale infrastructure cost reduction initiatives.

• Cross-archetype experience, such as JVM engineering combined with EKS or EMR experience.