Enterprise Ray Engineering
The open-source unified framework for scaling AI and Python applications, from training to serving. We design, optimize, and deploy production-grade architectures utilizing the full capabilities of the Ray ecosystem.
Ray
Production Certified
Ray is our framework of choice for scaling python code and deep learning workloads across cluster networks. We deploy Ray clusters to accelerate training runs, execute parallel tuning, and serve models at scale.
Our Ray Engineering Services
We deliver highly specialized, production-ready systems tailored to your technical requirements.
Ray Cluster Architecture
We deploy Ray clusters on Kubernetes (KubeRay), managing head nodes, worker pools, and auto-scaling rules.
Distributed Model Training
We scale PyTorch and TensorFlow training jobs across multiple GPU servers using Ray Train.
Hyperparameter Tuning
We automate hyperparameter sweeps using Ray Tune, optimizing neural network configurations quickly.
Ray Serve Model Hosting
We serve models in production using Ray Serve, configuring routing rules, batching requests, and scaling nodes.
Ray Tooling & Stack Integrations
We operate across the entire modern ecosystem surrounding Ray, deploying optimized dependencies and configurations.
Ray Libraries
Core Ray modules used to run distributed Python programs.
Cluster Operations
Tools used to deploy and monitor Ray clusters.
Integrations
Machine learning libraries scaled by Ray clusters.
Production-Grade Engineers
Top 1% Seniority
Experienced infrastructure engineers deploying distributed Ray clusters.
Immediate Velocity
Optimized training throughput using Ray Train and Ray Tune configurations.
Compliance Native
Expert knowledge in cluster scheduling, memory limits, and node configurations.
Proven Results with Ray
Explore how we leverage Ray to build business-critical platforms and achieve operational milestones.
Ray Cluster Accelerates LLM Fine-Tuning by 4x
We deployed a Ray cluster on AWS, distributing training runs across multiple GPU nodes, cutting execution times.
Distributed Forecast Pipeline Screens Warehouses Monthly
Built a forecasting engine using Ray Data, running parallel predictions across thousands of store inventories.
Flexible Engagement Models
Scale your engineering capacity dynamically. We integrate seamlessly into your operations with three battle-tested engagement models.
Staff Augmentation
Inject senior AI and MLOps engineers directly into your active squads. Rapidly scale resources with dedicated support under your management.
Dedicated Team
A self-governing team of engineers, project managers, and QA specialists built specifically to design, build, and support your proprietary AI pipelines.
Full Build & Deliver
Fixed-scope or milestone-driven development. We take ownership from requirements definition and MVP design to final production handoff.
Compliance-First Deployment Standards
We integrate safety controls natively. Every Ray application is architected to satisfy strict global compliance and privacy policies.
SOC 2 Type II
Logical isolation & logs
HIPAA PHI
PHI data de-identification
ISO/IEC 27001
International safeguards
Common Ray Questions
What is Ray, and when do you use it? expand_more
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Accelerate Your AI Project with Ray
Schedule an architectural blueprint review session with our senior Ray engineers to map your database, compliance, and MLOps strategies.