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About the role
About The Role We're a fast-moving startup building a high-traffic communication product and production-grade ML models. We're looking for a language-agnostic DevOps Lead to take complete ownership of our infrastructure from scratch — cloud environments, secure architectures, CI/CD, observability, and developer tooling that backend and ML engineers rely on daily. Responsibilities Architect, provision, and scale cloud infrastructure across GCP/AWS/Azure for both web backends and compute-intensive ML workloads Establish cloud accounts from scratch with strict IAM policies, RBAC, and secure networking (VPCs, subnets, peering) Build robust, language-agnostic CI/CD pipelines using GitHub Actions, Jenkins, or GitLab CI for safe multi-environment deployments Containerize and orchestrate applications using Docker, Kubernetes, and Helm Deploy and manage Matrix homeserver infrastructure (Synapse/Dendrite) including federation, workers, and database setup Build observability from a blank slate — monitoring, logging, alerting using Prometheus, Grafana, ELK, or Datadog Instrument applications for deep insights into API performance, system health, and ML model inference metrics Manage infrastructure as code with Terraform or Pulumi — version-controlled, repeatable, cloud-agnostic Collaborate with software engineers and ML researchers to remove deployment bottlenecks and maintain developer velocity Requirements 8+ years in DevOps, SRE, or Cloud Infrastructure with holistic infrastructure ownership Language-agnostic — comfortable navigating Python, Go, Node.js, Java codebases for tooling and instrumentation Deep hands-on experience with Kubernetes, Docker, and Helm in production Strong IaC proficiency (Terraform or Pulumi) Extensive CI/CD pipeline experience (GitHub Actions, Jenkins, GitLab CI) Security-first mindset — cloud networking, IAM/RBAC, secrets management Proven ability to build monitoring/logging stacks from scratch Builder's mentality — comfortable starting with a blank canvas and making authoritative architectural decisions Nice to Have Experience with MLOps — GPU provisioning, model registry integration, inference pipeline deployment Familiarity with Matrix protocol infrastructure (Synapse/Dendrite federation and worker architectures) Experience with MongoDB, PostgreSQL, Neo4j, and Redis in cloud environments Frontend deployment experience (CDN, static hosting, SSR)
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