Kubernetes Technical Support Engineer

About The Project

The project is an AI vision platform running on Kubernetes and used for enterprise video analytics. We are looking for a highly technical Tier 2 / Tier 3 Technical Support Engineer to help customers troubleshoot and resolve complex platform, infrastructure, and deployment issues.

Location & Schedule

LATAM-based candidates only.

Fully remote.

Must be available to work U.S. East Coast business hours.

Onboarding

During the first month, the engineer will focus on learning the platform and internal support processes, working closely with senior engineers before independently handling customer issues. The onboarding also includes internal training and certification related to the AI vision platform.

Responsibilities

Provide Tier 2 / Tier 3 technical support to enterprise customers.

Troubleshoot Kubernetes-based deployments, software, infrastructure, hardware, networking, and configuration issues.

Reproduce customer environments and run tests to diagnose technical problems.

Work directly with customers during technical discussions and troubleshooting sessions.

Manage and resolve technical support tickets.

Collaborate with Engineering, Sales, and Customer Success teams.

Escalate complex issues and product bugs to Engineering and help drive them to resolution.

Clearly communicate technical findings, workarounds, and resolutions to customers.

Requirements:

• 2+ years of technical support experience, preferably in Tier 2 / Tier 3 support.

• Strong hands-on Kubernetes experience in production environments.

• At least one relevant Kubernetes or cloud certification, such as CKA, CKAD, CKS, AWS, Azure, or equivalent.

• Strong troubleshooting skills across software, infrastructure, networking, and distributed environments.

• Experience working directly with customers in a technical capacity.

• Strong written and verbal English communication skills.

• Ability to independently investigate complex technical issues and collaborate with engineering teams.

Nice to Have

Experience With AWS, Azure, Or Other Cloud Platforms.

Linux troubleshooting experience.

Experience with containers, networking, monitoring, and logging.

Experience supporting enterprise software products.

Exposure to AI, computer vision, video analytics, or similar technologies.