DevOps / Platform Engineer (Cloud, Kubernetes, AI Infra)

Role overview Build and operate cloud platforms for production software, including systems with demanding AI workloads such as vector databases and real-time inference. The role spans multiple cloud regions and Kubernetes environments, with an emphasis on repeatable infrastructure, observability, security, cost control, and reliable releases.

Responsibilities - Design and maintain AWS or GCP infrastructure across regions, including networking and replication. - Operate production Kubernetes clusters with autoscaling, monitoring, and cost controls. - Create and maintain Terraform modules and infrastructure-as-code workflows. - Build CI/CD pipelines that include testing, security checks, and multi-region deployments. - Define service objectives and support observability and on-call operations. - Manage platform costs, including compute, GPU, and vector database usage. - Apply security controls such as secrets management, network segmentation, and audit logging. - Provide self-service platform capabilities that help engineering teams deploy independently.

Requirements - At least four years of production DevOps, platform engineering, or SRE experience. - Strong AWS or GCP knowledge, including VPCs, IAM, networking, load balancing, and multi-region replication. - Production Kubernetes experience with EKS, GKE, or comparable platforms, plus Helm and operators. - Terraform expertise covering module design, state management, drift detection, and multi-account or multi-region setups. - Experience with secure CI/CD systems such as GitHub Actions or GitLab CI. - Familiarity with observability platforms such as Prometheus, Grafana, Datadog, or ELK. - Linux skills and scripting with Bash and Python or Go. - Strong English communication for architecture discussions.

Nice to have - Experience operating AI workloads, including GPU scheduling and vector database capacity planning. - Familiarity with SOC 2-aligned controls and AI workload observability.