AI Production Infrastructure and Model Serving Engineer

Overview of infrastructure fundamentals for deploying AI models: CPU vs GPU trade-offs, containerizing applications, async inference patterns and secrets/config promotion. Focus on architectural choices and operational consequences.

Examines CPU, GPU, serverless and managed API trade-offs to choose deployments by cost, latency and workload patterns.

Covers containerizing AI apps: packaging, isolation, image hygiene and deployment best practices.

Explains queues, worker patterns and async inference to decouple request latency from heavy processing.

Details secrets, config management and environment promotion to maintain reproducible, secure pipelines.

Deep dive into Kubernetes for GPU workloads: Kubernetes primitives for AI services, GPU scheduling, NVIDIA GPU Operator and GPU telemetry/failure modes. Practical guidance for running GPU clusters in production.

Introduces Kubernetes primitives relevant to AI services: Deployments, StatefulSets, Services and AI-focused CRDs.

Covers GPU scheduling strategies: node pools, taints and tolerations for correct workload placement.

Describes the NVIDIA GPU Operator to manage drivers and GPU components declaratively.

Covers GPU telemetry: key metrics, common failure modes and practical investigation techniques.

Introduction to model serving with vLLM: serving architecture patterns, OpenAI-compatible endpoints, batching and KV-cache optimizations, plus routing and autoscaling strategies for production stacks.

Compares serving architecture patterns: centralized inference, microservices and edge serving according to latency and cost needs.

Introduces vLLM basics and establishing OpenAI-compatible endpoints for seamless client integration.

Describes techniques to boost throughput and reduce latency: batching, KV cache and runtime optimizations.

Covers designing the production stack: request routing, autoscaling policies and integration considerations.

Focus on reliability and release for AI services: health checks and load testing, blue/green and canary release strategies, provider fallback and a production readiness review process.

Sets up health checks and load tests to validate behavior under stress and determine breaking points.

Describes release approaches: blue/green, canary and rollback techniques to reduce deployment risk.

Covers provider fallback strategies and graceful degradation to preserve service during external incidents.

Provides a production readiness checklist and review to validate technical and operational criteria before exposing service to traffic.