Distributed Systems Engineer III

🔥 15 hours ago

🇪🇬 Egypt – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

⛓️ Blockchain Engineer

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Mozn

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2017

💼 Consulting

🏥 Healthcare

📦 Logistics

💰 $10M Series A on 2022-02

Consulting • Healthcare • Logistics

Mozn is a regional AI company building Arabic-native generative AI and enterprise AI platforms. It provides OSOS (an Arabic-first GenAI platform), FOCAL (a financial-crime and fraud detection platform), and customized AI solutions spanning language intelligence, risk intelligence, operational AI, data management, geospatial intelligence, and AI centers. Mozn focuses on serving enterprise customers in the MENA region (including healthcare, finance, and government) with SaaS products and tailored AI services that prioritize cultural relevance, data security, and regulatory compliance.

📋 Description

• 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

🎯 Requirements

• 4–7 years of experience in Platform Engineering, Infrastructure Engineering, Distributed Systems, SRE, Backend Engineering, Data Infrastructure, or a related field • Strong production experience with Apache Kafka — mandatory • Hands-on production experience with Kubernetes — mandatory • Strong experience with at least one of MySQL or PostgreSQL — mandatory • Solid understanding of distributed-systems fundamentals including replication, partitioning, consistency, availability, fault tolerance, scalability, and failure recovery • Experience with ArgoCD/GitOps and infrastructure-as-code such as Terraform • Experience operating workloads on a public cloud such as GCP, OCI, AWS, or Azure • Strong production troubleshooting and incident-resolution skills • Experience with automation or scripting using Python, Bash, Go, Java, or similar languages • Understanding of high-availability, disaster-recovery, and multi-tenant architecture fundamentals • Experience with observability and operational tooling such as Prometheus, Grafana, OpenSearch/ELK, LGTM, or equivalent • Strong understanding of infrastructure and networking fundamentals in cloud-native environments • Good-to-have: experience with Kafka Connect, Debezium, Kafka Streams, or CDC platforms • Good-to-have: experience with distributed analytical databases such as StarRocks, ClickHouse, Apache Doris, or similar • Good-to-have: experience with Flink, Spark, or other distributed data-processing systems • Good-to-have: experience executing large-scale data, database, application, or infrastructure migrations • Good-to-have: experience with active-active, multi-zone, or multi-region systems • Good-to-have: experience operating stateful workloads on Kubernetes • Good-to-have: experience supporting AI/ML infrastructure or GPU-based workloads • Good-to-have: experience with cloud networking, service mesh, ingress, load balancing, or storage platforms • Good-to-have: contributions to Kubernetes, Kafka, distributed-systems, or other open-source infrastructure projects

🏖️ Benefits

• Competitive compensation • Top-tier health insurance • Enabling culture • Responsibility and trust • Freedom and autonomy in decision-making • Fun and dynamic workplace • Opportunity to work alongside leading AI professionals • Inclusive and diverse workplace culture