Hire for Ray Serve Mastery
You need to deploy multiple ML models at scale, but building a custom serving infrastructure is complex and time-consuming. You're here because you need an engineer who can use Ray Serve to build a scalable, fault-tolerant, and flexible model deployment platform. You need an expert who can manage autoscaling, compose multiple models, and ensure high availability.
Sound Familiar?
Common problems we solve by providing true Ray Serve experts.
Are you manually scaling your model deployments based on guesswork?
The Problem
Manual scaling is inefficient, leading to either overprovisioning (wasted cost) or underprovisioning (high latency and dropped requests).
The TeamStation AI Solution
We find engineers who are experts in Ray Serve's built-in autoscaling, able to configure policies that automatically scale your model deployments up and down based on real-time traffic.
Proof: Autoscaling for cost and performance efficiency
Is your inference pipeline a complex chain of separate services?
The Problem
Chaining multiple models (e.g., a text classifier followed by a summarizer) with separate network calls is slow and inefficient.
The TeamStation AI Solution
Our engineers can use Ray Serve's model composition capabilities to build a single, unified inference graph that chains multiple models together in the same process, eliminating network overhead.
Proof: Low-latency model composition
Is your ML stack separate from your data processing stack?
The Problem
A disconnected ML and data stack makes it hard to build end-to-end applications.
The TeamStation AI Solution
We look for engineers who can leverage the entire Ray ecosystem, using Ray Data for preprocessing, Ray Train for distributed training, and Ray Serve for deployment, all in a single, unified Python framework.
Proof: A unified platform for end-to-end ML
Our Evaluation Approach for Ray Serve
For roles requiring deep Ray Serve expertise, our Axiom Cortex™ evaluation focuses on practical application and deep system understanding, not just trivia. We assess candidates on:
- Scaling model deployments horizontally and vertically
- Autoscaling policies based on traffic
- Composition of multiple models into a single graph
- Fault tolerance and high availability
- Integration with the broader Ray ecosystem (Ray Core, Ray Data)
Ready to Hire Elite Ray Serve Talent?
Stop sifting through unqualified resumes. Let us provide you with a shortlist of 2-3 elite, pre-vetted candidates with proven Ray Serve mastery.
Book a No-Obligation Strategy Call