Skip to main content

Learning Roadmap

How to Become a AI Infrastructure Engineer

A step-by-step, phase-based learning path from beginner to job-ready AI Infrastructure Engineer. Estimated completion: 8 months across 5 phases.

5 Phases
32 Weeks Total
High Entry Barrier
Advanced Difficulty
← AI Infrastructure Engineer Overview Interview Prep →
Your Progress 0 / 5 phases

Progress saved in your browser — no account needed.

  1. Foundations: Linux, Networking, and Cloud Basics

    4 weeks
    Goals
    • Achieve proficiency in Linux system administration including process management, storage, and shell scripting
    • Understand TCP/IP, DNS, load balancing, and VPN fundamentals relevant to distributed systems
    • Deploy and manage basic cloud infrastructure on AWS or GCP using the console and CLI
    Resources
    • Linux Upskill Challenge (linuxupskillchallenge.org)
    • AWS Cloud Practitioner or GCP Cloud Digital Leader certification materials
    • Kelsey Hightower's 'Kubernetes the Hard Way'
    Milestone

    You can provision a cloud VM, configure networking, and automate basic tasks with shell scripts

  2. Containers, Kubernetes, and Infrastructure as Code

    6 weeks
    Goals
    • Build production-grade Docker images including multi-stage builds and CUDA-aware containers
    • Deploy and manage Kubernetes clusters with Helm, understand RBAC, networking, and storage classes
    • Write Terraform or Pulumi modules to provision cloud infrastructure reproducibly
    Resources
    • Kubernetes documentation + 'Kubernetes in Action' by Marko Lukša
    • Terraform Up & Running by Yevgeniy Brikman
    • Docker official tutorials and NVIDIA Container Toolkit documentation
    Milestone

    You can deploy a multi-service application on Kubernetes with Terraform-managed infrastructure and CI/CD

  3. ML Fundamentals and GPU Computing

    6 weeks
    Goals
    • Understand ML training lifecycle - data loading, training loops, checkpointing, evaluation, and deployment
    • Learn GPU architecture fundamentals - CUDA cores, memory hierarchy, NVLink, multi-GPU communication via NCCL
    • Run distributed training experiments using PyTorch DDP or FSDP across multiple GPUs
    Resources
    • Fast.ai Practical Deep Learning course
    • NVIDIA DLI: Getting Started with Deep Learning
    • PyTorch Distributed documentation and tutorials
    Milestone

    You can launch a distributed training job on a multi-GPU cluster and debug common failure modes

  4. ML Platform Engineering and Model Serving

    8 weeks
    Goals
    • Build an end-to-end ML pipeline using Kubeflow Pipelines or Argo Workflows
    • Deploy model serving infrastructure with Triton Inference Server or vLLM including batching and auto-scaling
    • Implement ML experiment tracking, model registry, and artifact management with MLflow or W&B
    Resources
    • Kubeflow documentation and example pipelines
    • NVIDIA Triton Inference Server user guide
    • vLLM GitHub repository and documentation
    • Made With ML by Goku Mohandas
    Milestone

    You can build a self-service ML platform where data scientists train, track, and serve models end-to-end

  5. LLMOps, Advanced Scaling, and Production Hardening

    8 weeks
    Goals
    • Design and operate inference clusters for large language models including quantization, tensor parallelism, and continuous batching
    • Implement vector database infrastructure and RAG pipelines at production scale
    • Build comprehensive observability - GPU metrics, model drift detection, cost dashboards, and alerting
    • Master cost optimization strategies including spot/reserved mix, right-sizing, and workload-aware scheduling
    Resources
    • vLLM and TensorRT-LLM documentation for LLM serving
    • Anyscale Ray documentation for distributed inference
    • Cloud provider cost optimization whitepapers (AWS Well-Architected, GCP Architecture Framework)
    • Charity Majors' observability engineering resources
    Milestone

    You can design, cost-optimize, and operate a production AI platform serving billions of tokens per day with sub-second latency

Practice Projects

Apply your skills with hands-on projects. Ordered by difficulty.

GPU-Aware Kubernetes Cluster Setup

Beginner

Provision a Kubernetes cluster on a cloud provider (GKE or EKS) with GPU node pools, install the NVIDIA device plugin, and deploy a simple PyTorch inference pod that utilizes GPU resources. This builds foundational understanding of how GPU resources are managed in containerized environments.

~20h
Kubernetes administrationGPU resource managementContainer orchestration

MLflow Experiment Tracking Server on Kubernetes

Beginner

Deploy an MLflow tracking server on Kubernetes with a PostgreSQL backend and S3 artifact store. Run several ML experiments that log parameters, metrics, and model artifacts. Register a model and transition it through staging and production states.

~15h
ML experiment trackingModel registry managementHelm deployment

Terraform Module Library for ML Infrastructure

Intermediate

Build a set of reusable Terraform modules that provision a complete ML environment: VPC, EKS/GKE cluster with GPU and CPU node pools, S3/GCS data buckets, IAM roles for ML services, and a monitoring stack. Support dev/staging/prod promotion via workspace or directory structure.

~35h
Infrastructure as CodeTerraform module designCloud IAM and security

End-to-End ML Pipeline with Kubeflow or Argo

Intermediate

Build a multi-step ML pipeline that includes data validation, feature engineering, distributed model training, evaluation with quality gates, and automated deployment to a serving endpoint. Parameterize the pipeline so different teams can run it with different datasets and model configurations.

~40h
Pipeline orchestrationDistributed trainingModel evaluation automation

Real-Time LLM Inference Cluster with vLLM

Advanced

Deploy a vLLM-based inference service for a 7B-13B parameter LLM with continuous batching, auto-scaling based on queue depth, and Prometheus/Grafana monitoring. Implement request routing, rate limiting, and graceful degradation. Load test with realistic traffic patterns and optimize for p99 latency targets.

~45h
LLM serving architectureAuto-scaling configurationPerformance optimization

Multi-Tenant ML Platform with Resource Isolation

Advanced

Build a self-service ML platform on Kubernetes where multiple teams can submit training jobs and deploy inference endpoints with strict resource isolation. Implement namespace-based isolation, GPU quota management using Kueue or custom controllers, cost attribution per team, and a developer portal with custom CRDs.

~60h
Multi-tenancy designKubernetes operator developmentResource scheduling

RAG Infrastructure Pipeline with Vector Databases

Intermediate

Design and implement a retrieval-augmented generation infrastructure including document ingestion, chunking, embedding computation, vector database indexing (Pinecone, Weaviate, or pgvector), and a serving endpoint that combines retrieval with LLM generation. Include monitoring for retrieval quality and latency.

~35h
Vector database managementEmbedding pipeline designRAG architecture

Distributed Training on a Multi-Node GPU Cluster

Advanced

Set up a multi-node GPU cluster with high-bandwidth networking (InfiniBand or RoCE) and train a large model (e.g., Llama 7B) using FSDP or DeepSpeed ZeRO Stage 3. Implement checkpointing with fault tolerance, monitoring of communication overhead, and automatic job resubmission on preemption.

~50h
Distributed training orchestrationNCCL debuggingCheckpoint management

Ready to Start Your Journey?

Prep for interviews alongside your learning — it reinforces every concept.

Practice Interview Questions Explore More Careers