Platform Engineer

### Who you are
- 2+ years in platform engineering, DevOps, SRE, or backend infrastructure roles
- Strong Kubernetes experience (deployment, debugging, scaling — not just `kubectl apply`)
- Hands-on with infrastructure-as-code: Terraform, Helm, or Pulumi
- Experience with at least one major cloud provider (AWS preferred; GCP or Azure also valued)
- Proficiency in one or more of: Go, Python, Java. Comfortable reading and contributing to backend codebases
- Working knowledge of CI/CD systems (GitHub Actions, Bazel, ArgoCD, or similar)
- Solid fundamentals in Linux, networking, and distributed systems
- Experience deploying or operating ML inference workloads (model serving, GPU scheduling, vLLM, TensorFlow Serving, or similar)
- Familiarity with streaming/messaging systems (Kafka, Pulsar) and data stores (MariaDB/PostgreSQL, Aerospike, ClickHouse, OpenSearch)
- Experience with GitOps workflows (ArgoCD, Flux)
- Exposure to air-gapped or on-premises Kubernetes deployments
- Background in observability tooling (Prometheus, Grafana, OpenTelemetry, Datadog)
- Experience providing technical support or working directly with enterprise customers on infrastructure issues
- Comfort with AI-assisted development workflows and managing AI coding agents

### What the job involves
- You'll own the infrastructure that runs our deploy anywhere platform — from Kubernetes clusters serving ML inference at scale to the CI/CD pipelines, IaC, and observability stack that keep it all reliable
- This is a hands-on role: you'll write Helm charts and Terraform one day, debug a Kafka consumer lag issue the next, and ship a backend service feature the day after
- You'll deploy across AWS, GCP, and on-premises (including air-gapped environments), and you'll participate in an on-call rotation supporting enterprise customers
- Build and maintain infrastructure-as-code (Terraform, Helm) for our AWS EKS and GCP GKE clusters, plus on-premises deployments (including Tanzu and air-gapped environments)
- Own CI/CD pipelines (GitHub Actions, Bazel, ArgoCD) and drive GitOps adoption
- Deploy, scale, and optimize ML/NLP inference workloads (vLLM, PyTorch, GPU scheduling with various Kubernetes scalers)
- Build and improve observability: Prometheus, Grafana, Datadog,, and OpenTelemetry
- Collaborate with Field Engineering to support PoCs and platform deployments in customer cloud VPCs and on-prem environments
- Contribute to backend services (Java 21, Python, gRPC) and platform features
- Improve system reliability, scalability, and developer experience across the engineering org

### Benefits
- Comprehensive medical insurance
- Life & disability insurance
- Employee assistance program
- Free gym access at HQ
- Annual global summit
- Free snacks and beverages
- Bereavement leave
- Monthly stipend for phone/internet
- Maternity/Paternity leave
- Minimal meeting Fridays
- Flexible working hours
- Unlimited vacation time policy
- Home office stipend for remote employees