Hire for Ray Serve Mastery
Ray is the universal framework for **scaling Python workloads**, and Ray Serve is its specialized engine for production machine learning inference. You need an engineer who can think in terms of distributed systems but write simple, Pythonic code. Our vetting identifies experts who use Ray to transform single-node Python applications and complex ML models into scalable, fault-tolerant production services, breaking the performance bottlenecks that are holding you back.
Sound Familiar?
Common problems we solve by providing true Ray Serve experts.
ML Inference is More Than Just a Single Model
The Problem
Your production inference logic requires a complex graph of steps: data validation, preprocessing, multiple model calls, and post-processing business logic. Orchestrating this with traditional microservices is a slow, high-latency nightmare.
The TeamStation AI Solution
A Ray Serve expert builds a single, unified inference service. They compose a graph of different models (from any framework) and Python code into one deployable unit, drastically reducing network overhead and simplifying the entire architecture.
Proof: Reduce end-to-end inference latency for complex model pipelines by 75%.
The Barrier Between a Python Script and a Scalable Service
The Problem
Your Python application works perfectly on a single machine, but you have no clear path to scale it out to handle production traffic. You are facing a complete rewrite or a deep dive into complex distributed systems frameworks.
The TeamStation AI Solution
Our Ray specialists use simple decorators (`@ray.remote`) to effortlessly distribute your existing Python code across a cluster. They make scaling intuitive, allowing your team to focus on application logic, not infrastructure.
Proof: Transition a single-node Python application to a distributed system with <10% code modification.
Inefficient Resource Management for ML Models
The Problem
Each of your ML models is deployed as a separate service, each with its own expensive, underutilized GPU. You cannot efficiently pack multiple models onto a single machine or dynamically scale them based on traffic.
The TeamStation AI Solution
A TeamStation Ray Serve engineer implements dynamic, multi-model serving. They can deploy multiple models within a single service, dynamically allocate resources, and even autoscale replicas (including scaling to zero) to maximize resource utilization and minimize cost.
Proof: Improve GPU utilization by 3-5x by co-locating multiple models on a single server.