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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.
Role
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.
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
Location: Fully remote (Spain‐based)
Key Responsibilities
Infrastructure & Cloud
Design, build, and maintain multi‐cloud infrastructure on AWS and GCP
Operate and optimize Kubernetes clusters (GKE, EKS) at scale (up to ~1 K nodes)
Lead infrastructure modernization and cloud migration initiatives
Implement cost optimization strategies across cloud providers
Automation & CI/CD
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
Platform Services
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
Reliability & Observability
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
What We Offer
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
Work Culture
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 ******.
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