DataOps Intern

AI in this role

vllm

ml-ops

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.

Key Responsibilities

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

Skills, Knowledge & Expertise

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

How we rate this

DataOps Intern at Tabby rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ML OpsvLLM

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
Walk me through how you've used vLLM in your day-to-day work.
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: ML Ops and vLLM. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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