Embedding VC

Embedding VC

Cloud & IoT Service Engineer

Los Angeles, CA

Sponsorship not specifiedDetected 184 days ago
PythonGitAWSCloud PlatformsDockerKubernetesTerraformCI/CDJenkinsLinuxPrometheusGrafanaDevOpsRESTKafkaComplianceTest AutomationVoIPCollaboration

About the role

  • Company Overview Join a global leader in cloud-native solutions and IoT innovation, driving secure and scalable platforms across industries.

Responsibilities

  • Optimize Kubernetes clusters and edge computing infrastructure for real-time IoT telemetry and predictive maintenance use cases.
  • Design CI/CD pipelines using Jenkins and GitLab CI, integrating automated testing and deployment for global teams.
  • Develop Python/Shell scripts for IoT data validation, log analysis, and cross-region backup solutions.
  • Design and develop automated tools for deployment, monitoring, and fault recovery using Python/Shell, tailored to IoT and cloud infrastructure needs (e.g., log analysis scripts, backup automation).
  • Implement data encryption (TLS) and RBAC policies, ensuring GDPR and USA's Cybersecurity Law compliance.

Nice to have

  • AWS Certified DevOps Engineer/Solutions Architect.

Skills

  • 3+ years in cloud operations (AWS preferred) with Million-User Scale platform.
  • Proficiency in Linux, Kubernetes, Jenkins, and containerization tools (Docker).
  • Strong scripting skills in Python (e.g., automation, RESTful API integration) and Shell.
  • Experience with Terraform, Prometheus, Grafana, or big data tools (Athena, Kafka).
  • Understanding of IoT protocols (MQTT,H2, WebRtc) and Edge-Cloud Collaboration.
  • Competitive salary with performance bonuses.
  • Relocation support and flexible remote work options.
  • Health insurance covering international assignments.
  • Access to cross-regional training programs (e.g. AWS certification, USA's cybersecurity compliance).
  • Cloud & IoT Platform Operations

Compensation

  • Competitive salary with performance bonuses.

This listing is sourced directly from Embedding VC's careers page and normalized into a canonical job model.