Kubernetes Engineer → AI Engineer / MLOps Engineer

Kubernetes Engineer to AI: Orchestrating Intelligent Workloads

Transition from Kubernetes engineering to AI/MLOps by leveraging your container orchestration expertise for machine learning infrastructure. Your deep understanding of cluster management, resource scheduling, and distributed systems provides an exceptional foundation for running AI workloads at scale. Kubernetes has become the de facto platform for ML infrastructure, from training distributed models across GPU nodes to serving predictions with auto-scaling inference endpoints. This path focuses on GPU scheduling and NVIDIA device plugins, distributed training orchestration, KubeFlow for ML pipelines, and production model serving with KServe. You will learn to manage the unique challenges of AI workloads: GPU memory management, checkpoint storage, model versioning, and the bursty traffic patterns of inference services. Your experience with Operators, Helm charts, and GitOps practices translates directly to managing ML platform components. The path bridges your infrastructure expertise with AI fundamentals, ensuring you understand both the workloads you are orchestrating and how to optimize Kubernetes for them. By the end, you will be positioned for MLOps Engineer or AI Platform Engineer roles, combining infrastructure excellence with machine learning operational knowledge. Timeline: 4-6 months.

4-6 months

Difficulty: Intermediate

Prerequisites

Kubernetes cluster administration and troubleshooting
Helm chart development and management
Kubernetes Operators and Custom Resource Definitions
Container networking and service mesh concepts
Resource management (requests, limits, quotas)
GitOps workflows (ArgoCD or Flux)

Your Learning Path

AI/ML Fundamentals for Infrastructure Engineers

2-3 weeks

Skills You'll Build

How neural networks and LLMs workTraining vs inference workload characteristicsGPU architecture basics (CUDA cores, VRAM, tensor cores)ML lifecycle: experimentation, training, servingCommon ML frameworks (PyTorch, TensorFlow, JAX)

GPU Orchestration on Kubernetes

3-4 weeks

Skills You'll Build

NVIDIA device plugin and GPU operator setupGPU resource scheduling and sharing (MIG, time-slicing)Node affinity and taints for GPU workloadsGPU memory management and monitoringMulti-GPU and multi-node training configurations

KubeFlow and ML Pipelines

3-4 weeks

Skills You'll Build

KubeFlow installation and architectureKubeFlow Pipelines for ML workflowsKatib for hyperparameter tuningKubeFlow Notebooks for data science teamsIntegration with MLflow for experiment tracking

Distributed Training Infrastructure

3-4 weeks

Skills You'll Build

Kubernetes Job and CronJob patterns for trainingPyTorchJob and TFJob operatorsDistributed data parallel training conceptsCheckpoint storage with PVCs and object storageTraining job monitoring and failure recovery

Model Serving and Inference

3-4 weeks

Skills You'll Build

KServe (formerly KFServing) deployment patternsTriton Inference Server on KubernetesCanary deployments for model rolloutsAutoscaling inference workloads (HPA, KEDA)Model registry integration and versioning

LLM Infrastructure and RAG Systems

3-4 weeks

Skills You'll Build

Running LLMs on Kubernetes (vLLM, TGI)Vector database deployment (Milvus, Weaviate on K8s)RAG architecture for enterprise applicationsLLM gateway and rate limiting patternsCost optimization for LLM inference

Portfolio and MLOps Role Preparation

2-3 weeks

Skills You'll Build

End-to-end ML platform project showcaseDemonstrating K8s + ML infrastructure expertiseMLOps interview preparationSystem design for ML platformsSalary negotiation for AI infrastructure roles

Kubernetes Engineer to AI: Orchestrating Intelligent Workloads

Prerequisites

Your Learning Path

AI/ML Fundamentals for Infrastructure Engineers

🎁 The AI Engineer Starter Kit

GPU Orchestration on Kubernetes

KubeFlow and ML Pipelines

Distributed Training Infrastructure

Model Serving and Inference

LLM Infrastructure and RAG Systems

Portfolio and MLOps Role Preparation

🎁 The AI Engineer Starter Kit

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