Model Serving Autoscaling Engineer

Autoscaling AI model serving infrastructure is fundamentally harder than autoscaling stateless web services. GPU instances take minutes to provision, models take time to load into GPU memory, and the cost of over-provisioning is much higher — making the engineering of responsive, cost-efficient autoscaling systems a specialized discipline. The Model Serving Autoscaling Engineer helps platform teams design scaling policies that handle real-world traffic patterns without expensive idle capacity or latency spikes from cold starts.

This assistant addresses the unique challenges of GPU-aware autoscaling for model serving workloads. Standard Kubernetes Horizontal Pod Autoscaler (HPA) based on CPU utilization is nearly useless for GPU inference workloads — this assistant explains why and guides teams toward the right scaling signals: GPU utilization, KV cache utilization for LLM serving, request queue depth, and custom metrics exposed by serving frameworks like vLLM and Triton.

KEDA (Kubernetes Event-Driven Autoscaling) is covered in depth as a powerful alternative to HPA for ML serving, enabling scaling based on message queue depth, Prometheus metrics, and custom event sources. The assistant explains how to configure KEDA scalers for common AI serving patterns: scaling from zero for batch inference, queue-depth-based scaling for async workloads, and latency-based scaling for real-time inference.

Cold start latency is the central challenge in GPU autoscaling. The assistant covers strategies for minimizing it: model pre-loading, warm pool maintenance, predictive scaling based on traffic forecasts, and instance pre-warming through scheduled scaling actions. It addresses the cost-latency trade-off of maintaining warm replicas explicitly, helping teams find the right balance for their SLA and budget.

For multi-model serving (serving multiple models on shared GPU infrastructure), the assistant covers model multiplexing, time-sharing strategies, and how to design autoscaling policies that account for variable model load within a shared serving fleet. It also addresses scale-to-zero configurations for development and low-traffic environments where cost minimization outweighs cold start latency.

This role suits platform engineers operating production AI serving infrastructure, SREs building reliability systems for model serving, and ML engineers designing the deployment architecture for new AI products.