DataOps Intern

About the company Tabby builds financial products used by millions of users across the GCC. The infrastructure behind them runs at scale, under strict requirements for reliability, cost efficiency and regulatory compliance. This is not a course and not a shadowing programme. It is an engineering role with real responsibility. Context The Data Platform team runs the infrastructure that AI, ML and data workloads at Tabby depend on: compute, orchestration, deployment, observability and cost control across cloud environments. The work sits between classic DevOps and the machine-learning side. The same clusters, pipelines and monitoring that keep a service alive also keep models trained, served and measured. The internship is designed for strong early-career engineers who are comfortable in Linux and a cloud, and who already use AI tools in their own work rather than reading about them. Interns join the team, work on real production infrastructure under senior review and are expected to meet engineering standards from day one. What you will do This is not a helper or ticket-closing role. Interns work on real production tasks under senior review. - Work with the cloud infrastructure (primarily GCP) and the bare-metal fleet that host our data, ML and AI workloads - Build and maintain CI/CD pipelines for services and models - Run and troubleshoot containerised workloads on Kubernetes - Set up and improve monitoring, alerting and logging, and act on what they show - Automate repetitive operational work with Python or Bash instead of repeating it - Support model training and inference workloads: environments, resources, deployment, cost - Investigate incidents in infrastructure and pipelines and help find root causes - Improve the reliability and cost efficiency of the platform - Work within SAMA regulatory requirements: in Saudi fintech, where data lives and who can reach it is part of the engineering problem, not paperwork someone else handles What you will actually work with Not a wish list. This is the stack the team runs today. Nobody is expected to arrive knowing all of it. - Data: CDC pipelines, BigQuery, Airflow - ML: Airflow, ClearML and similar orchestration and experiment tooling - AI: bare-metal GPU servers, vLLM, open-source models served in-house - Platform: GCP, Kubernetes, Linux, networking - Context: SAMA regulations Required - Solid Linux fundamentals: filesystem, processes, permissions, networking basics, comfortable in the shell - Hands-on experience with at least one cloud provider, evidenced by something you actually built or deployed. We run on GCP, so GCP experience is the most directly useful, but AWS or Azure evidence counts: the concepts transfer, and we would rather have someone who has really built something on one cloud than someone who has clicked around ours - Understanding of networking: DNS, TCP/IP basics, load balancing, what happens between a request and a service - Working knowledge of containers, and enough Kubernetes to deploy and debug a workload - Familiarity with monitoring and observability concepts: metrics, logs, alerts and what makes an alert useful - Python or Bash sufficient to automate operational tasks - Experience with Git and standard development workflows - Real, current use of AI tools in your own engineering work: which tools, for what, and an informed view of which models suit which task. We would rather hear an honest comparison than a list of names - Structured thinking and attention to correctness - Open to constructive feedback - English sufficient for documentation and team communication Strong plus - Infrastructure as code (Terraform or similar) - Experience running a CI/CD system end to end (GitLab CI, GitHub Actions or similar) - An observability stack in practice: Prometheus, Grafana or equivalents - Exposure to MLOps tooling: experiment tracking, model registries, feature stores, inference serving - Has tried to run an open-source model themselves (on a laptop, a rented GPU, anything) and can explain how LLMs actually work rather than just which API they called - Any experience with GPU workloads, or with the cost side of running them - Interest in platform design and developer experience Eligibility - Saudi nationals only - We welcome both current students and fresh graduates - We expect a full-time level of engagement. The programme is not part-time. Students can align time for classes or exams with their mentor in advance, but performance, ownership and involvement are expected at a full-time level - Six months, starting autumn 2026 - Paid internship, funded by Tabby - Full integration into an engineering team - Distributed engineering team across multiple countries - A path to a junior role on the platform side afterwards. That is our intent and what we aim for, not a guarantee: it depends on how the internship goes This internship is intentionally demanding and designed for candidates aiming for fast professional growth in infrastructure and

Solid Linux fundamentals: filesystem, processes, permissions, networking basics, comfortable in the shell; Hands-on experience with at least one cloud provider (GCP preferred, AWS or Azure acceptable); Understanding of networking (DNS, TCP/IP basics, load balancing); Working knowledge of containers and Kubernetes; Familiarity with monitoring and observability concepts (metrics, logs, alerts); Python or Bash for automation; Experience with Git and standard development workflows; Real, current use of AI tools in your own engineering work; Structured thinking and attention to correctness; Open to constructive feedback; English sufficient for documentation and team communication

Work on real production tasks under senior review; Manage cloud infrastructure (primarily GCP) and bare-metal fleet hosting data, ML and AI workloads; Build and maintain CI/CD pipelines for services and models; Run and troubleshoot containerised workloads on Kubernetes; Set up and improve monitoring, alerting and logging; Automate repetitive operational tasks with Python or Bash; Support model training and inference workloads (environments, resources, deployment, cost); Investigate incidents in infrastructure and pipelines and help find root causes; Improve the reliability and cost efficiency of the platform; Ensure compliance with SAMA regulatory requirements