platform engineer in fintech

Tabby builds financial products used by millions of users across the GCC. Its infrastructure operates at scale under strict requirements for reliability, cost efficiency, and regulatory compliance.

• Work with cloud infrastructure, primarily GCP, and bare-metal infrastructure hosting data, ML, and AI workloads;
• Build and maintain CI/CD pipelines for services and models;
• Run and troubleshoot containerized workloads on Kubernetes;
• Set up and improve monitoring, alerting, and logging, and act on the findings;
• Automate repetitive operational work with Python or Bash;
• Support model training and inference workloads, including environments, resources, deployment, and cost;
• Investigate infrastructure and pipeline incidents and help identify root causes;
• Improve platform reliability and cost efficiency;
• Work within SAMA regulatory requirements concerning data location and access.

• Solid Linux fundamentals, including filesystems, processes, permissions, networking basics, and shell usage;
• Hands-on experience with at least one cloud provider, demonstrated through something built or deployed;
• Understanding of DNS, TCP/IP basics, load balancing, and request-to-service flow;
• Working knowledge of containers and enough Kubernetes to deploy and debug workloads;
• Familiarity with monitoring and observability concepts, including metrics, logs, and alerts;
• Python or Bash skills sufficient to automate operational tasks;
• Experience with Git and standard development workflows;
• Current practical use of AI tools in engineering work, with the ability to explain tool and model choices;
• Structured thinking and attention to correctness;
• Openness to constructive feedback;
• English sufficient for documentation and team communication;
• Saudi nationals only;
• Current student or fresh graduate status;
• Full-time level of engagement; the programme is not part-time;
• Nice to have: infrastructure as code with Terraform or similar, end-to-end CI/CD experience with GitLab CI or GitHub Actions, practical observability stack experience with Prometheus or Grafana, exposure to MLOps tooling, experience running open-source models, GPU workload or GPU cost experience, interest in platform design and developer experience.

• Paid six-month internship starting autumn 2026;
• Full integration into an engineering team;
• Distributed engineering team across multiple countries;
• A path to a junior platform role afterwards, depending on internship performance.