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Cloud Engineer / DevOps – Cloud Engineering

Applike Group
Remote (Worldwide)
Remote

About this role

About the Role We are hiring for a Cloud Engineer / DevOps/SRE to join adjoe's Cloud Engineering team within Applike Group. You will design and operate the cloud infrastructure that powers our large-scale ad tech platform, keeping the system online, fast, and cost-efficient, with a stack centered on Kubernetes, real-time data pipelines, and open-source tooling. What You'll Do

  • Design, deploy, and operate cloud infrastructure at scale across data centers and cloud regions, with hands-on work on Kubernetes, Kafka, Druid, Airflow, Spark, Terraform, and GitLab.
  • Manage Kubernetes clusters with event-driven autoscaling (Karpenter) and real-time streaming via Kafka; provision and monitor infrastructure as code through Terraform.
  • Contribute to our cost-aware architecture using open-source components instead of managed AWS services where appropriate, including monitoring with Prometheus, Grafana, and logging with Fluent Bit.
  • Ensure reliable CI/CD pipelines that are fast, predictable, and maintainable, enabling smooth software delivery.
  • Collaborate with backend teams building high-performance Go services across distributed environments; support microservice communication patterns using topics, queues, and object storage.
  • Migrate workloads to self-hosted solutions when beneficial and help introduce new tools to boost business results. What We're Looking For
  • 3+ years in DevOps, SRE, or Platform Engineering, with hands-on experience deploying and operating containerized apps in Kubernetes.
  • Deep AWS expertise (EKS, DynamoDB, SNS, SQS, IAM, Lambda) and strong IaC skills with Terraform.
  • Familiarity with microservice event-driven architectures, messaging, and distributed storage patterns.
  • Experience building reliable CI/CD pipelines (GitLab or equivalent).
  • Comfort migrating workloads to self-hosted/open-source alternatives and advocating for cloud-agnostic solutions. Nice to Have
  • Proficiency in Go and building high-performance services.
  • Experience with Druid, Airflow, Spark, Karpenter, Prometheus, Grafana, Elasticsearch, Fluent Bit, and ML tooling.
  • Familiarity with data pipelines and real-time ML inference workflows. Compensation & Benefits
  • Not disclosed at this time.

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