company_site
DataOps Intern
Tabby
KSA
Internship
Intern
Onsite
Discovered 1 weeks ago
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
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Ready to ScanKey skills for this role
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
Key Skills for This Role
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
Full Job Posting
What you will do
- 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 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
What you will actually work with
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