Note: The job is a remote job and is open to candidates in USA. EverOps is an embedded service provider that partners with customer engineering teams to address mission-critical infrastructure, cloud, and delivery challenges. The Lead Kubernetes Platform Engineer will lead a large-scale Amazon EKS modernization discovery and subsequent execution program, covering platform architecture, upgrade automation, compute optimization, Graviton migration, cost engineering, and operational toil reduction. The role also serves as a player-coach and primary technical partner to customer infrastructure leadership.
Responsibilities
- Estate Discovery: Build a complete baseline of a multi-cluster production EKS estate, covering cluster inventory, topology, workload placement, ownership, per-cluster cost, and Kubernetes version and support status
- Blast Radius & Architecture: Analyze failure domains, isolation boundaries, and dependency concentration in very large clusters, and design a multi-cluster target architecture with clear workload placement and tenancy models
- Upgrade Automation: Design and implement an upgrade approach (in-place vs. blue/green clusters, Karpenter drift-based node rotation, add-on and API deprecation management) that turns a months-long manual cycle into a repeatable, largely automated process
- Compute & Capacity Strategy: Lead instance sizing and workload-fit analysis across instance families, generations, and node sizes, accounting for DaemonSet overhead, bin-packing, network limits, and headroom for rapid scale-up
- Graviton Migration: Plan and drive a phased ARM64 migration across Java, Go, PHP, and Python workloads, including multi-architecture builds, native dependency remediation, and per-service rightsizing against latency SLOs
- Karpenter Engineering: Configure NodePools, weights, and node overlays so scheduling favors price-performance rather than hourly price, and design safe Spot patterns that protect interruption-sensitive and stream-processing workloads
- Cost Engineering: Model compute, support, and commitment economics (Savings Plans, On-Demand, Spot suitability by workload tier, extended support exposure) and quantify savings ranges by lever
- Platform Posture: Assess and improve ingress (including migration off Ingress NGINX toward Gateway API), service mesh, CNI, GitOps, and infrastructure-as-code posture across the estate
- Operability & Toil Reduction: Quantify maintenance and toil burden using the customer’s own engineering data, set reduction targets, and build the automation that gives time back to platform teams
- AI-Native Operations: Define the scope and success criteria for an AI-assisted DevOps agent workstream, and assess platform readiness for AI-accelerated development demand
- Technical Leadership: Lead the TechPod as a player-coach, run the working cadence with the customer’s infrastructure owner, and partner with AWS specialists on capacity planning and architecture decisions
- Documentation & Readouts: Produce the estate baseline, architecture designs, upgrade plans, roadmap, and executive readout, and present findings and recommendations to engineering leadership
Skills
- 8+ years in DevOps, SRE, Platform, or Infrastructure Engineering, including 4+ years operating production Kubernetes and prior experience in a technical lead, staff, or principal-level role
- Deep production experience with Amazon EKS at large scale (multiple production clusters, thousands of nodes, or tens of thousands of pods), with a working understanding of where control plane, scheduling, networking, and API limits start to bite
- Hands-on ownership of EKS version upgrades across multiple production clusters, including API deprecations, add-on compatibility, node rotation, and rollback planning, plus a working knowledge of the EKS standard and extended support lifecycle
- Advanced production experience with Karpenter (v1+), including NodePools, disruption and consolidation, weighting, and instance-type flexibility
- Strong understanding of EC2 instance families and generations, CPU architecture differences, network performance limits, and how workload bottlenecks shift when the underlying compute changes
- Experience migrating production workloads to Graviton or other ARM64 platforms, including multi-arch container builds and performance validation
- Experience modeling compute costs and commitments (Savings Plans, Reserved Instances, Spot) and building savings cases grounded in real usage data
- Solid knowledge of the AWS VPC CNI, ingress controllers, Gateway API, service mesh, and load balancing on EKS
- Advanced proficiency with Terraform, plus experience with Helm and GitOps tooling such as Argo CD or Flux
- Comfortable using Datadog, Prometheus, Grafana, or comparable tooling to evaluate cluster health, capacity, and workload performance
- Strong scripting ability using Python, Go, or Bash to automate infrastructure and operational workflows
- Demonstrated ability to enter an unfamiliar environment, surface what isn't documented, and produce a defensible current-state picture and roadmap in weeks rather than quarters
- Ability to translate technical findings into cost, risk, and capacity terms that engineering leadership and executives can act on
- Experience with high-traffic B2C platforms with sharp daily peaks, or with real-time, safety-critical services where availability at peak is non-negotiable
- Experience designing cell-based or multi-cluster architectures to reduce blast radius, including fleet management and workload placement across clusters
- Experience running performance-per-dollar comparisons across instance generations and architectures and feeding the results into scheduling policy
- Experience operating Spot capacity for large production fleets, including interruption handling for Kafka consumers and other stateful or stream-processing workloads
- Experience tuning JVM heap, garbage collection, and Kubernetes requests/limits when moving workloads to new instance generations or architectures
- Experience running Bottlerocket or similar, including node boot-time optimization
- Experience building AI agents or LLM-driven tooling for infrastructure operations, upgrades, or toil reduction
- Experience leading assessment or discovery engagements that end in a committed, funded program of work
- CKA, CKS, AWS Certified Solutions Architect – Professional, AWS Certified DevOps Engineer – Professional, or similar advanced certifications
Benefits
- 100% Remote Workplace: We’ve been remote since Day 1!
- Unlimited Paid Time Off.
- Equity: Become a true owner of the company.
- 401K with company contribution and sponsored healthcare.
- Professional Growth: Access to training and certification programs to accelerate your career.
Company Overview