You might be our missing piece if you have:

• Proven experience in creating and maintaining production delivery pipelines using Cloud Build and Cloud Deploy for GCP workloads (managing triggers, approvals, rollbacks).
• Strong knowledge of GCP networking (VPCs, subnets, Private Service Connect), IAM roles, Secret Manager, and Workload Identity Federation.
• Hands-on experience with Infrastructure as Code using Config Connector/Config Controller (KRM) or Google Cloud Infrastructure Manager.
• Proficiency in GKE (Helm optional), Cloud Run, and container image management with Artifact Registry.
• Practical experience with observability tools such as Cloud Monitoring, Cloud Logging, and Managed Service for Prometheus.
• Scripting skills with gcloud, Bash, or Python for automation.
• At least 5 years of experience in a similar role.
• Excellent communication skills and a collaborative mindset.
• The ability to understand and present complex ideas to stakeholders.
• A passion for continuous learning and staying updated with industry trends.
  
  

We would be thrilled if you have:

• Experience with the specifics of AI/ML projects.
• Familiarity with Dataproc, Vertex AI, or Databricks on GCP.
  
  

We will be working together on:

• Developing, managing, and maintaining CI/CD pipelines using Cloud Build and/or GitHub Actions, enabling continuous integration and deployment for containerized applications.
• Building, deploying, and scaling Kubernetes-based applications using Helm charts on Google Kubernetes Engine (GKE).
• Configuring and managing cloud infrastructure using Terraform, ensuring scalable and secure environments for clients.
• Implementing monitoring and logging solutions with Cloud Monitoring, Cloud Logging, and Grafana Dashboards to track system performance and application metrics.
• Collaborating closely with cross-functional teams, including development and QA, to deliver efficient and reliable code deployment.