Cloud Infrastructure Specialist (DevOps)

Role Overview

We are seeking an experienced technical specialist to manage client-side Kubernetes platform deployments and infrastructure automation. In this role, you will bridge customer-facing technical support with backend engineering, ensuring seamless integration across multi-cloud and on-premises environments.

Key Responsibilities

• Platform Integration: Install and configure enterprise platform solutions inside customer-managed, CNCF-compliant Kubernetes clusters across AWS, Azure, GCP, and hybrid setups.
• Infrastructure & Automation: Develop and optimize deployment scripts using Terraform, Ansible, and Helm charts.
• Technical Troubleshooting: Partner with client engineering teams to resolve intricate networking, container, and infrastructure bottlenecks.
• L3 Technical Escalations: Serve as the final escalation point for complex technical issues routed from internal support teams.
• Cross-Functional Feedback: Collaborate with core Product and R&D teams to translate field insights into product features.
• Documentation: Build comprehensive technical guides, KB articles, and operational playbooks for internal and client use.

Requirements

• Experience: 3+ years in client-facing DevOps, SRE, or Cloud Systems Architecture.
• Container Systems: Deep hands-on proficiency in managing, troubleshooting, and logging CNCF Kubernetes environments.
• Cloud Infrastructure: Strong command of major cloud providers (AWS/GCP/Azure), core Linux administration, and enterprise networking.
• Automation Tools: Practical expertise using Terraform, Helm, and Ansible for Infrastructure as Code.
• Scripting: Solid Python programming skills for script writing, debugging, and process automation.
• Communication: Exceptional verbal and written communication skills to explain complex architecture to technical and business audiences.

Preferred Qualifications

• Certifications: Certified Kubernetes Administrator (CKA) required within 6 months of hire.
• Domain Expertise: Background in supporting AI, Data Science, or Big Data infrastructure platforms.
• Data Stores: Foundational operational familiarity with Redis, PostgreSQL, and MongoDB.