About Tabby
Tabby builds financial products serving millions across the GCC with infrastructure running at scale under strict reliability, cost efficiency and regulatory compliance requirements. This is a real engineering role with genuine responsibility, not a shadowing programme.
The Role
Join the Data Platform team managing infrastructure for AI, ML and data workloads. You'll work on production systems spanning compute, orchestration, deployment, observability and cost control across cloud environments—bridging classic DevOps and machine learning operations.
What You'll Do
- Work with GCP cloud infrastructure and bare-metal GPU servers hosting data, ML and AI workloads
- Build and maintain CI/CD pipelines for services and machine learning models
- Deploy, run and troubleshoot containerised workloads on Kubernetes
- Set up monitoring, alerting and logging systems; act on insights they reveal
- Automate operational tasks using Python or Bash
- Support model training and inference: environments, resources, deployment and cost optimization
- Investigate infrastructure incidents and identify root causes
- Improve platform reliability and cost efficiency while meeting SAMA regulatory requirements
Technology Stack
Data: CDC pipelines, BigQuery, Airflow
ML: Airflow, ClearML orchestration and experiment tooling
AI: Bare-metal GPU servers, vLLM, open-source models
Platform: GCP, Kubernetes, Linux, networking
Context: SAMA financial regulations
Required Skills
- Solid Linux fundamentals: filesystem, processes, permissions, networking, shell comfort
- Hands-on cloud experience (GCP, AWS or Azure): something you built and deployed
- Networking knowledge: DNS, TCP/IP, load balancing, request-to-service flow
- Container and Kubernetes proficiency: deploy and debug workloads
- Monitoring and observability concepts: metrics, logs, alerts
- Python or Bash for operational automation
- Git and standard development workflows
- Active, current use of AI tools in your own engineering work with informed model selection
- Structured thinking and attention to correctness
- English for documentation and team communication
Strong Advantages
- Infrastructure as code (Terraform or similar)
- End-to-end CI/CD system experience (GitLab CI, GitHub Actions)
- Observability stack hands-on experience (Prometheus, Grafana)
- MLOps tooling exposure: experiment tracking, model registries, inference serving
- Personal experience running open-source models and understanding LLM mechanics
- GPU workload or cost management experience
Programme Details
- Six-month internship starting autumn 2026
- Paid, full-time engagement with full team integration
- Distributed engineering team across multiple countries
- Potential path to junior platform engineering role based on performance
- Saudi nationals only; current students and fresh graduates welcome
This is an intentionally demanding programme designed for candidates seeking rapid professional growth in infrastructure and AI platform engineering.
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