Senior Backend Engineer (Kubernetes)

This role is open to candidates based in LATAM, Africa, and Eastern Europe. Please note that as this role supports U.S.-based clients, candidates must be available to work during U.S. business hours aligned with the client’s time zone. Our client is a well-funded, early-stage AI company building a large-scale data platform used to train, fine-tune, and evaluate the next generation of AI models. As the platform continues to scale, they are seeking a deeply technical backend engineering specialist to build reliable, high-throughput distributed systems.Role OverviewThe Senior Backend Engineer (Kubernetes) will design, build, and operate the backend services that power a large-scale, high-throughput data platform.This is a specialist engineering role focused on backend development and distributed systems rather than generalist software development or people management. The Senior Backend Engineer will work directly with engineering leadership on system architecture, reliability, scalability, observability, containerized production environments, and deployment infrastructure.The ideal candidate brings exceptional computer science fundamentals, deep distributed systems expertise, and significant backend engineering experience. This person will also contribute to the broader engineering team by sharing technical context, supporting less-experienced colleagues, and delegating work effectively.LocationFully Remote | 9:00 AM - 5:00 PM ESTKey ResponsibilitiesBackend Services & APIsDesign, develop, and maintain scalable backend services and <APIs.Build> backend systems using a microservices <architecture.Support> the backend infrastructure required for a large-scale, high-throughput data platform.Distributed Systems ArchitectureApply distributed systems principles to the design and operation of production <systems.Design> systems with consideration for consistency, partitioning, replication, consensus, and failure <handling.Build> fault-tolerant, highly available, and scalable distributed systems.Kubernetes & Container OrchestrationBuild and operate production workloads on Kubernetes.Manage deployments, services, ingress, autoscaling, and <Helm.Work> with managed Kubernetes environments such as <EKS.Support> containerized production environments using Docker.Performance & ReliabilityOptimize application performance across distributed and containerized environments.Improve the reliability of backend systems and services.Identify opportunities to strengthen system scalability and <availability.CI/CD> & DeploymentDesign and implement robust CI/CD <pipelines.Support> GitOps-based deployment practices.Implement Kubernetes-native deployment workflows.ObservabilityImprove observability across distributed backend <services.Support> effective system monitoring and alerting.Strengthen visibility into the health and performance of production systems.Architecture & Technical CollaborationPartner directly with engineering leadership on architectural decisions.Share technical knowledge and context with less-experienced colleagues.Delegate work appropriately to support effective team execution.Contribute collaboratively to technical decisions and overall engineering quality.Responsible AI UseUse AI tools as part of daily engineering workflows to improve <productivity.Select> appropriate AI models for planning and architectural work.Orchestrate AI-assisted execution efficiently while critically reviewing outputs.Follow established AI governance practices.Ensure access flows through approved API endpoints and MCPs rather than direct database access or other shortcuts.QualificationsExperience10+ years of professional software engineering experience with a strong backend focus.Deep hands-on experience designing and working with distributed systems.3+ years of hands-on Kubernetes experience running production workloads.Experience working with managed Kubernetes environments such as EKS, GKE, or AKS.Experience designing and developing microservices architectures.Experience working with relational and/or NoSQL databases.SkillsExceptional computer science fundamentals with a deeply architectural and systems-minded approach.Strong understanding of distributed systems concepts, including consistency models, partitioning, replication, consensus, CAP theorem, and failure handling.Proficiency in at least one modern backend programming language, with Go or Python strongly preferred and C++ or Rust also applicable.Strong hands-on Kubernetes capabilities across deployments, services, ingress, autoscaling, Helm, and production workload management.Experience working with Docker and containerized applications.Strong understanding of microservices architecture and inter-service communication patterns.Familiarity with infrastructure-as-code, with Terraform preferred.Ability to use AI tools effectively in daily engineering work while critically reviewing their output and following defined governance requirements.Exposure to Elasticsearch or other search technologies is a plus.Experience with GitOps tools such as Argo CD or Flux is a plus.Knowledge of service mesh technologies such as Istio or Linkerd is a plus.Experience with workflow orchestration tools such as Temporal or Airflow is a plus.Experience with observability technologies such as Prometheus, Grafana, OpenTelemetry, or ELK is a plus.Experience building or operating search-heavy systems is a plus.Contributions to open-source projects related to Kubernetes or distributed systems are a plus.What Success Looks LikeScalable backend services and APIs reliably support the company's high-throughput data platform.Distributed systems are designed with strong fault tolerance, availability, and scalability.Kubernetes production workloads operate reliably and efficiently.Application performance and reliability continuously improve across distributed and containerized <environments.CI/CD> and GitOps processes support reliable Kubernetes-native deployments.Monitoring, alerting, and observability provide clear visibility into distributed production systems.Architectural decisions reflect strong computer science and distributed systems fundamentals.Technical knowledge and context are effectively shared across the engineering <team.AI> tools increase engineering productivity while remaining within established governance requirements.OpportunityThis role offers the opportunity to apply deep backend and distributed systems expertise to a large-scale data platform supporting the training, fine-tuning, and evaluation of AI models. The Senior Backend Engineer (Kubernetes) will work directly with engineering leadership on architecture, scalability, reliability, Kubernetes, deployment infrastructure, and observability while contributing technical knowledge across a collaborative engineering team.Application Process:To be considered for this role these steps need to be followed:Fill in the application formRecord a video showcasing your skill sets