Saudi Arabia · المملكة العربية السعودية
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Saudi Arabia
English
المملكة العربية السعودية
العربية
Crux engineers scalable enterprise AI platforms — MLOps infrastructure, ML lifecycle management, and intelligent data pipelines — that move Saudi organizations from AI experimentation to AI production at national scale. نبني منصات الذكاء الاصطناعي للمؤسسات السعودية · SDAIA Aligned · Vision 2030
SageMaker · Azure ML · Kubeflow · Ray · Vertex AI — deployed on AWS Saudi me-south-1, Azure KSA, or on-premises GPU clusters with NDMO data sovereignty
MLflow · Weights & Biases · Neptune — experiment tracking, hyperparameter optimization, and model comparison for Saudi AI research and production teams
Centralized model artefact management — versioning, lineage tracking, A/B deployment, champion-challenger comparison, and automatic rollback for production AI systems
Feast · Tecton — centralized feature computation and serving, ensuring training-serving consistency and eliminating feature duplication across Saudi enterprise AI teams
Triton · Seldon · BentoML — high-performance model serving with GPU acceleration, dynamic batching, and auto-scaling for Saudi enterprise workloads at national scale
Evidently · WhyLogs · custom dashboards — data drift detection, model performance degradation, and automated retraining triggers for production AI reliability
Design the end-to-end AI platform architecture for Saudi enterprises — compute strategy (GPU/TPU), storage (data lake, feature store), ML toolchain, serving infrastructure, and governance layer — aligned to SDAIA and NDMO requirements.
Build CI/CD for machine learning — automated training pipelines, model evaluation gates, canary deployments, shadow mode testing, and automatic rollback — enabling Saudi AI teams to deploy new model versions daily without manual intervention.
Build high-throughput data pipelines that feed Saudi AI platforms — Apache Spark, Apache Kafka, dbt, Airflow — processing structured and unstructured data at petabyte scale with real-time streaming capability.
Manage the complete machine learning lifecycle — from data versioning and experiment tracking to model registry, deployment orchestration, performance monitoring, and automated model retraining when drift is detected.
Deploy high-performance AI inference infrastructure — Triton Inference Server, NVIDIA TensorRT, GPU acceleration, dynamic batching, horizontal auto-scaling, and low-latency API endpoints for Saudi enterprise production workloads.
Build AI platforms that natively support Arabic language models — Arabic NLP training pipelines, RTL text processing, Arabic model fine-tuning infrastructure, and Arabic benchmark evaluation datasets for Saudi-specific AI applications.
We had 14 AI models stuck in Jupyter notebooks — none in production. Crux built our MLOps platform in 11 weeks. All 14 models are now live, serving 31 million predictions per day, and our AI team ships new models weekly instead of quarterly. This is what Saudi AI at scale looks like.
MLOps. Feature stores. Model serving. Arabic AI. SDAIA aligned. Crux builds the AI platforms that move Saudi Arabia from AI pilot to AI production — at 48 million predictions per day.