Kubernetes Platform Engineer

Job Description

Job Description

Kubernetes Platform Engineer requires:

• Voice Services Modernization

• Expert-level Kubernetes administration, deployment, lifecycle management, and troubleshooting
• Strong Ansible experience including role and playbook development
• CI/CD pipeline development and automation experience
• Cloud platform administration (AWS, Azure, GCP, OpenStack)
• Infrastructure-as-Code using Terraform
• Experience with monitoring, logging, dashboarding, and alerting platforms
• Scripting or programming experience (Python preferred)
• Production applications support and operational troubleshooting
• Understanding of high-availability and low-latency application architectures
• Red Hat OpenShift experience
• AI/ML platform exposure or AI-assisted operations tooling
• Telecom or voice application experience (SIP, SBCs, IMS, voice middleware)
• Experience supporting cloud-native migrations from VM environments
• GitOps and modern deployment methodologies
• Experience with observability platforms such as Prometheus, Grafana, ELK, Splunk, or Datadog
• Outcome / Business Objective
• Deliver a production-ready, Kubernetes-based runtime environment for voice middleware and backend services that improves scalability, resilience, automation, and operational efficiency while minimizing service disruption and migration risk.

• Proven track record leading end-to-end modernization projects that migrated production VM‑based middleware/backend services (preferably voice or other latency‑sensitive apps) into Kubernetes-based production environments — delivered migration planning, staged cutovers, and successful production rollouts. (JD implies senior-level scope; years below.)
• Hands‑on experience operating and running production Kubernetes platforms for high‑availability, low‑latency services — delivered platform lifecycle management, troubleshooting/incident response, operational runbooks, and knowledge transfer to operations teams.
• Delivered automation and release pipelines that enabled repeatable deployments and infrastructure lifecycle management in production — produced CI/CD pipelines, infrastructure‑as‑code modules, and automation/playbooks to support testing, deployment, and production cutover activities