Mozn

Blockchain Engineer

• Build, operate, and continuously improve production cloud-native platforms running distributed workloads
• Work hands-on with Kubernetes, including upgrades, node pools, workload lifecycle, troubleshooting, and platform operations
• Deploy and manage workloads using ArgoCD, Helm, GitOps, Terraform, and automation
• Design and operate systems focused on scalability, availability, resilience, performance, and operational simplicity
• Troubleshoot complex issues across Kubernetes, cloud infrastructure, networking, storage, applications, and distributed services
• Operate and troubleshoot Apache Kafka in production across high-throughput and distributed workloads
• Manage Kafka topics, partitions, replication, consumer groups, retention, throughput, latency, and failure recovery
• Integrate Kafka with databases and applications using Kafka Connect, Debezium, or similar CDC/event-streaming platforms
• Operate and troubleshoot MySQL and/or PostgreSQL, including replication, high availability, backup, recovery, performance, and migrations
• Support data-intensive workloads and analytical platforms such as StarRocks, ClickHouse, Apache Doris, or similar technologies
• Design and operate resilient platforms across node, service, zone, and infrastructure failures
• Implement and validate backup, recovery, disaster recovery, failover, and business-continuity capabilities
• Apply multi-zone, multi-region, and active-active architecture principles where appropriate
• Build multi-tenant platforms with appropriate isolation, scalability, resource management, and reliability
• Participate in disaster-recovery exercises, failure simulations, migrations, and resilience initiatives
• Automate infrastructure and platform lifecycle operations using Terraform, Python, Bash, Go, or similar technologies
• Build reliable deployment and GitOps workflows and reduce manual operational effort
• Implement monitoring, logging, alerting, and observability for distributed workloads
• Participate in production incident response, root-cause analysis, and long-term reliability improvements
• Contribute infrastructure for AI, machine-learning, and data-intensive workloads
• Evolve cloud and Kubernetes platforms for AI workloads, data pipelines, model-serving infrastructure, and platform services
• Address compute, GPU, networking, storage, data movement, observability, and workload-isolation requirements for AI platforms
• Build reusable platform capabilities with engineering teams to run AI and data workloads reliably at scale
• Stay current with infrastructure patterns across AI platforms, distributed data systems, and cloud-native technologies

Distributed Systems Engineer III