Job Description

As a member of the Region Flexibility Migration (RFM) team, you will be responsible for driving Amazon's DRAM memory optimization initiative focused on reducing the memory footprint of EKS clusters through end-to-end cluster management, container-level memory profiling, and resource optimization. You will launch, maintain, and optimize EKS clusters while performing deep memory analysis of containerized workloads to identify and eliminate memory waste across Amazon's Kubernetes infrastructure.

This is a high-impact, independently-driven role where you will manage the full lifecycle of EKS clusters, from provisioning and configuration through memory profiling, optimization testing, and production validation. You will analyze pod-level memory consumption, identify inefficient resource requests/limits, optimize node pool configurations, and implement container-level memory optimizations that achieve meaningful DRAM savings across distributed systems.

Key Responsibilities

• Manage end-to-end EKS cluster operations, including cluster provisioning, node group configuration, autoscaler tuning, and ongoing maintenance, while implementing memory-optimized configurations across managed node groups and Fargate profiles.
• Perform deep memory profiling of containerized workloads running on EKS: analyzing pod-level memory consumption, identifying over-provisioned resource requests/limits, detecting memory leaks, and understanding how memory is utilized across distributed microservices architectures.
• Implement and validate memory optimization strategies at the container and cluster level, including resource request/limit right-sizing, Vertical Pod Autoscaler (VPA) configuration, node pool instance type optimization, and memory-efficient scheduling policies.
• Analyze and optimize how memory is allocated and consumed across distributed systems running on Kubernetes, including container runtime overhead, kernel memory accounting, cgroup memory limits, shared memory segments, and inter-pod communication overhead, to identify hidden memory waste at the infrastructure layer.
• Perform custom operations and iterative experiments using Amazon internal tooling to validate optimization impact: own end-to-end deployment, test execution, metric validation, and derive actionable insights from results.
• Design and execute testing strategies for EKS optimizations, including load testing, soak testing, and chaos engineering experiments to validate that memory-optimized configurations maintain reliability and performance under production-like conditions.
• Collaborate with service teams to review cluster architectures, discuss findings, propose optimization plans, and align resolution strategies while communicating effectively across engineering leadership and technical stakeholders.
• Develop comprehensive operational runbooks, SOPs, documentation, and technical specifications that capture EKS optimization patterns and can be consumed by both human engineers and AI agents to orchestrate optimization workflows at scale.

A Day in the Life

Your morning might begin by analyzing Kubernetes metrics for an EKS cluster: examining pod memory utilization against requested resources, identifying containers running at 20% of their memory limits, and correlating node-level DRAM consumption with workload patterns. By mid-day, you are running a controlled experiment: adjusting resource requests, deploying VPA recommendations to a staging namespace, and stress-testing the optimized configuration to validate stability.

After lunch, you are on a call with a service team, walking them through your memory profiling findings: showing which pods are over-provisioned, where memory leaks exist, and proposing a phased right-sizing approach that protects their availability SLAs. Before wrapping up, you are codifying what you learned into a reusable spec: one that another engineer (or an AI agent) could pick up and apply to a similar EKS workload tomorrow.

You operate with high autonomy, own your end-to-end investigations, and thrive on making container orchestration systems leaner without breaking them.

Required Qualifications

• Bachelor's degree in computer science, Engineering, or equivalent technical field.
• 5-7+ years of hands-on experience launching, managing, and optimizing EKS/Kubernetes clusters in large-scale production environments, with end-to-end ownership of cluster lifecycle operations.
• Deep expertise in Kubernetes resource management, including pod resource requests/limits, node affinity/anti-affinity, autoscaling (HPA, VPA, Cluster Autoscaler/Karpenter), and memory-aware scheduling, with demonstrable experience in resource right-sizing.
• Direct experience with memory optimization or resource right-sizing of EKS/Kubernetes clusters in production, including VPA implementation, bin-packing optimization, and node pool consolidation strategies.
• Strong understanding of memory allocation in distributed containerized systems, including container runtime memory overhead, Linux cgroup memory accounting, OOM-kill behavior, kernel memory, and how memory is consumed across microservices communicating via service mesh or direct networking.
• Proficiency in container memory profiling tools and techniques, including Prometheus/Grafana metrics, kubectl top, cAdvisor, and the ability to correlate pod-level metrics with node-level DRAM utilization to identify optimization opportunities.
• Proficiency in using generative AI tools and assistants as part of daily engineering workflows to accelerate problem-solving, code development, and technical analysis.
• Advanced English proficiency is required.

Preferred Qualifications

• Experience with Amazon internal tools including Amazon Profiler, CloudWatch Container Insights, X-Ray, and load/stress testing frameworks for validating optimization impact under production-like conditions.
• Experience with testing strategies for Kubernetes optimizations, including load testing, chaos engineering (e.g., Litmus, Gremlin), and canary deployments to validate memory-optimized configurations.
• Experience developing AI agents or automated workflows that can orchestrate tasks, extract information from services, and coordinate optimization activities across multiple systems.
• Strong attention to detail and effective communication abilities: able to present findings, propose strategies, and influence service team stakeholders and engineering leadership.
• Familiarity with CI/CD pipelines, deployment automation, and Amazon deployment technologies and best practices for infrastructure changes.

About Accenture

Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.

Visit us at www.accenture.com

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