<p>We’re looking for a Senior Cloud Engineer to design, build, and operate secure, scalable infrastructure on Google Cloud Platform (GCP). You’ll lead with GKE for container orchestration, Infrastructure as Code (Terraform or Ansible) for repeatability, and scripting (Python, Shell/Bash) to automate everything from provisioning to observability. This role bridges architecture and hands-on delivery, partnering closely with DevOps, SRE, Security, and application teams.</p><p><br></p><p>What You’ll Do</p><ul><li><strong>Architect & implement</strong> highly available, cost-efficient GCP environments (VPCs, subnets, routing, load balancers, Cloud NAT, Cloud DNS, Cloud Storage, Cloud SQL/Spanner/BigQuery as applicable).</li><li><strong>Design, deploy, and operate GKE</strong> clusters (node pools, autoscaling, upgrades, ingress, CNI/CNI‑overlays, workload identity, network policies, pod security).</li><li><strong>Automate infrastructure</strong> with Terraform or Ansible (modules/roles, workspaces/environments, pipelines, policy-as-code).</li><li><strong>Build platform tooling</strong> and automation in Python/Shell/Bash (provisioning, configuration drift remediation, release packaging, operational runbooks).</li><li><strong>Implement observability</strong> (Cloud Monitoring/Logging, Prometheus/Grafana, OpenTelemetry) and actionable alerting/SLOs.</li><li><strong>Harden security</strong> (IAM least-privilege, service accounts, secrets management, private clusters, image scanning, workload identity, org policies).</li><li><strong>Enable CI/CD</strong> for apps and infra (Cloud Build/GitHub Actions/GitLab CI, artifact registries, blue/green or canary deployment strategies).</li><li><strong>Drive reliability</strong>—capacity planning, performance tuning, backup/DR strategies, incident response, root cause analysis, and postmortems.</li><li><strong>Mentor engineers</strong>, codify best practices, and contribute to architectural standards and roadmaps.</li></ul><p><br></p>
<p>We’re seeking a Senior AI/ML Engineer to build and scale real-time data pipelines, drive platform reliability, and lead core AI/ML engineering initiatives. You’ll work across teams to deliver high‑performance, secure, and cost‑efficient data and machine learning systems.</p><p><br></p><p>Responsibilities:</p><ul><li>Architect and maintain scalable data pipelines (GCP, Kafka, Flink).</li><li>Own reliability, observability, and performance of the data/ML platform.</li><li>Optimize infrastructure performance and cloud costs.</li><li>Standardize data modeling and testing (dbt or similar).</li><li>Implement Infrastructure as Code and automation best practices.</li><li>Ensure security, governance, and compliance across data systems.</li><li>Lead cross-functional initiatives and mentor engineering peers.</li><li>Drive continuous improvement in tools, processes, and standards.</li></ul><p><br></p><p><br></p>
