Engineer - Cluster Management

Tata Consultancy Services; Seattle (WA); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Comprehensive Medical Coverage

401K Plan

Certification & Training Reimbursement

Job summary

Tata Consultancy Services is seeking a specialist to lead memory-optimized EKS cluster operations in a Kubernetes environment. The role focuses on profiling container memory, right-sizing resources, and implementing VM/VM- or VPA-based strategies to maximize efficiency across distributed microservices.

The candidate will design testing strategies (load, soak, chaos) and collaborate with engineering leadership to ensure resilient, scalable infrastructure while producing comprehensive runbooks and

Qualifications

• Experience managing EKS/Kubernetes clusters.
• Strong memory profiling and DRAM optimization skills.
• Expertise in resource right-sizing and performance tuning.
• Solid understanding of Linux containers, cgroups, OOM and runtime memory.
• Experience with Prometheus, Grafana, and CloudWatch monitoring.

Responsibilities

• Manage end-to-end EKS cluster operations including provisioning and maintenance with memory-optimized configs.
• Perform deep memory profiling of containerized workloads and identify memory leaks or waste.
• Implement and validate memory-optimization strategies at container and cluster level.
• Analyze memory allocation across distributed systems, including runtime overhead and inter-pod communication.
• Conduct experiments and testing (load/soak/chaos) to validate reliability under production-like conditions.
• Collaborate with service teams to review architectures and communicate findings to leadership.
• Develop runbooks, SOPs, and technical specs for scalable optimization workflows.

Skills

EKS/Kubernetes

Memory profiling

Resource right-sizing

Linux container internals

Monitoring/Observability

Job description

Job Description

• Resource Right‑Sizing & Performance Optimization
• Monitoring & Observability (Prometheus/Grafana/CloudWatch)
• 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 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.

Must Have Technical/Functional Skills

• EKS/Kubernetes Cluster Management
• Memory Profiling & DRAM Optimization
• Resource Right‑Sizing & Performance Optimization
• Linux Container Internals (cgroups/OOM/Runtime Memory)
• Monitoring & Observability (Prometheus/Grafana/CloudWatch)

Roles & 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 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.

TCS Employee Benefits Summary

• Discretionary Annual Incentive.
• Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
• Family Support: Maternal & Parental Leaves.
• Insurance Options: Aut& Home Insurance, Identity Theft Protection.
• Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
• Time Off: Vacation, Time Off, Sick Leave & Holidays.
• Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.

Salary Range

$120,000-$160,000 a year