Senior Devops

About Us We build and operate a fully-automated Speech Analytics SaaS platform running on Kubernetes across AWS and GCP. Our infrastructure processes ~160,000 hours of audio monthly with 99%+ uptime SLA, serving enterprise customers with mission-critical analytics needs. Our platform is built on modern, cloud-native technology: Kubernetes, Argo ecosystem, MongoDB, ElasticSearch, and 100% Terraform-driven Infrastructure as Code. We auto-scale from dozens to over 1,000 kubernetes nodes based on demand. Beyond our core SaaS product, we deliver managed solutions (Autopilot and Copilot platforms) and build AI-based services packaged as containerized, Terraform-ready modules for seamless integration into customer cloud environments (AWS, GCP, Azure). We're a team that values strong engineering practices, automation-first mindset, and operational excellence. About the Role We're looking for a Senior DevOps / Platform Engineer to help design, automate, and operate our cloud-native platform. You'll work across AWS and GCP, manage Kubernetes at scale, implement highly-automated CI/CD workflows, and collaborate with engineering teams to ensure reliable delivery of SaaS features and AI-driven products. What Makes This Role Unique Real ownership and autonomy – you'll be a key technical decision-maker. Work directly with leadership on platform strategy. Hands‑on with cutting‑edge cloud‑native and AI/ML workloads. Opportunity to lead a major AWS / GCP migration to optimize costs and performance. This role is ideal for someone who thrives in high‑automation environments, enjoys solving complex platform challenges, and wants a visible impact on products used by enterprise customers. Location Fully remote (Spain‑based) Key Responsibilities Design, build, and maintain multi‑cloud infrastructure on AWS and GCP. Operate and optimize Kubernetes clusters (GKE, EKS) at scale (up to ~1K nodes). Lead infrastructure modernization and cloud migration initiatives. Implement cost optimization strategies across cloud providers. Manage Argo Workflows and ArgoCD for GitOps‑based deployments. Build and maintain end‑to‑end Infrastructure as Code with Terraform (modularized, reusable, multi‑cloud). Develop internal automation tooling and scripts (Python, Bash, Go). Implement zero‑downtime deployment strategies. Deploy and manage production MongoDB, ElasticSearch, and other core services. Package and deploy workloads using Helm, Docker, and GitOps pipelines. Ensure 99%+ uptime SLA through robust monitoring and incident response. Support delivery of AI containerized solutions ready for customer environments. Build comprehensive observability across all platform components. Implement security best practices and compliance requirements. Drive post‑incident reviews and continuous improvement. Requirements Must Have 5+ years as a DevOps, SRE, or Platform Engineer in production environments. Strong hands‑on Kubernetes experience (GKE and/or EKS) managing clusters at scale. Expert‑level Terraform and Infrastructure as Code workflows. Multi‑cloud experience with both AWS and GCP. Proven experience with CI/CD, GitOps, ArgoCD, Argo Workflows. Solid Docker and Helm expertise for containerized deployments. Strong scripting/programming skills in Python and Bash. Experience running production‑grade, scalable, and secure cloud systems. Comfortable with incident response and on‑call responsibilities. Nice to Have Programming for tooling development (Python, bash, Go, ...). Experience with observability stacks (Prometheus, Grafana, Elastic, OpenTelemetry). Hands‑on with AI/ML workloads in containerized environments. MongoDB and ElasticSearch operations at scale. Experience with cost optimization strategies in cloud environments. Contributions to open‑source DevOps/platform projects. AWS/GCP certifications. Compensation & Benefits Competitive salary package. Fully remote work with flexible hours. 23 days of vacation + Spanish public holidays. Growth & Impact: Real ownership – your decisions shape the platform's future. Work directly with leadership on technical strategy. Continuous learning with modern cloud‑native, DevOps, and AI tooling. Opportunity to mentor and grow the team as we scale. Visible impact on products used by enterprise customers. Engineering‑driven culture that values automation and best practices. Async‑first communication (we respect work‑life balance). Blameless post‑mortems and learning from incidents. Regular team knowledge‑sharing sessions and open cooperation. Interview Process Initial call (30 min). Technical interview (60 min). Final interview. Timeline Typically 2–3 weeks from application to offer. How to Apply Apply here or send your CV and a brief note about what excites you about this role to ******. #J-18808-Ljbffr