Xperteez Technology

Staff DevOps Engineer | Hybrid - Full Time

Xperteez Technology Yerevan, Armenia

Business Consulting and Services · 51-200 employees

9 h ago
devops Senior (5-10 yrs) Full-time Armenia
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About the role

You will own the reliability and scalability of production infrastructure, including multi-region and multi-cloud deployments. Additionally, you will lead incident response, optimize real-time streaming services, and manage CI/CD pipelines to ensure high availability.

What they look for

DevOps Kubernetes AWS Terraform Ansible CI/CD Jenkins GitHub Actions Prometheus Grafana Linux Python Networking Observability Cloud Architecture MLOps

Requirements

Candidates must have 5+ years of hands-on DevOps experience with highly available production systems and deep expertise in Kubernetes and cloud platforms. Proficiency in Infrastructure-as-Code, monitoring tools, and scripting languages is essential for this role.

Full description

Job Title: Staff DevOps Engineer

Location: Armenia

Role Type: Hybrid

As a Staff DevOps Engineer, you will own the reliability and scalability of the infrastructure behind our production products, including speech APIs, real-time streaming services, and voice AI platforms. You will be deeply involved in high-impact projects across the company.

This means hands-on work on highly available production systems on AWS, Azure, Google Cloud and on-prem, and leading architecture decisions for multi-region, multi-cloud and private cloud deployments. You will work closely with engineering, SDK and product teams.

What you’ll do

  • Work on the reliability of core production services end-to-end, with the goal of delivering and maintaining a 99.9% SLA
  • Design and lead infrastructure architectures for multi-region, multi-cloud and private cloud / on-premise deployments for enterprise customers
  • Design and build failover mechanisms, including auto-failover between regions and cloud providers
  • Build monitoring, alerting and observability tooling that helps us detect issues fast, support incident management, and keep historical monitoring data
  • Improve stability and latency of real-time streaming services (WebSocket-based), and optimize network cost and latency across regions
  • Own deployment of new ML models and new product features to production together with engineering teams
  • Participate in and lead production incident response, root cause analysis, and post-incident improvements
  • Design, implement and maintain CI/CD pipelines (Jenkins, GitHub Actions)
  • Manage patching and security across systems, and integrate security tools into our infrastructure
  • Drive DevOps best practices, standards and documentation across the team.

What we are looking for

Must-have

  • 5+ years of hands-on DevOps experience, including ownership of highly available production systems
  • Proven experience designing and operating systems with strict SLA requirements (99.9% or higher)
  • Deep production experience with Kubernetes (EKS preferred) and containers, including migrations from other orchestration platforms
  • Strong experience with at least one major cloud provider (AWS preferred); experience with multi-region and multi-cloud architectures
  • Hands-on experience with Infrastructure-as-Code tools (Terraform and/or Ansible)
  • Experience designing CI/CD pipelines with Jenkins and GitHub Actions
  • Strong experience with monitoring and observability stacks (Prometheus, Grafana, or similar) and incident management processes
  • Solid fundamentals in networking (TCP/IP, DNS, load balancing, VPNs, firewalls, CDN)
  • Strong Linux system administration skills
  • Proficiency with scripting languages (bash, Python, or similar) for automation
  • Strong proficiency in using Claude Code and AI-assisted coding workflows.

Nice-to-have

  • Experience with private cloud or on-premise deployments for enterprise customers
  • Experience with real-time / low-latency streaming systems (WebSocket, WebRTC, gRPC)
  • Experience deploying and serving ML models in production (GPU workloads, inference platforms)
  • Experience with network cost optimization in cloud environments.

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