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Learning Roadmap

How to Become a AI Runtime Engineer

A step-by-step, phase-based learning path from beginner to job-ready AI Runtime Engineer. Estimated completion: 6 months across 6 phases.

6 Phases
24 Weeks Total
Medium Entry Barrier
Advanced Difficulty
← AI Runtime Engineer Overview Interview Prep →
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  1. Systems & Infrastructure Foundations

    4 weeks
    Goals
    • Build strong Linux administration skills including process management, networking, and shell scripting
    • Master Docker containerization: writing Dockerfiles, multi-stage builds, and container networking
    • Gain fluency in Python for scripting, automation, and API development
    • Understand basic networking concepts: TCP/IP, DNS, load balancing, HTTP/gRPC protocols
    Resources
    • The Linux Command Line (William Shotts)
    • Docker Deep Dive (Nigel Poulton)
    • Python for DevOps (Noah Gift, Kennedy Behrman)
    • KodeKloud Docker and Kubernetes labs
    Milestone

    You can containerize a Python application, expose it via a REST API, and deploy it on a local Docker setup with proper networking.

  2. Cloud Infrastructure & Kubernetes

    4 weeks
    Goals
    • Provision and manage GPU instances on AWS (EC2 P-series), GCP (A2/A3), and Azure (NC/ND series)
    • Deploy and operate Kubernetes clusters with GPU support using NVIDIA device plugin
    • Write Helm charts and Kubernetes manifests for stateful and stateless AI workloads
    • Implement infrastructure-as-code with Terraform for reproducible AI environments
    Resources
    • Certified Kubernetes Administrator (CKA) study material
    • AWS GPU instance documentation and Deep Learning AMIs
    • Terraform Up & Running (Yevgeniy Brikman)
    • NVIDIA GPU Operator documentation
    Milestone

    You can provision a GPU-enabled Kubernetes cluster on a major cloud provider, deploy a containerized ML application, and manage it with Terraform.

  3. Model Serving Fundamentals

    4 weeks
    Goals
    • Understand inference concepts: batch vs. real-time, latency vs. throughput, cold start optimization
    • Deploy models using HuggingFace TGI, TorchServe, and NVIDIA Triton Inference Server
    • Implement REST and gRPC inference endpoints with proper request validation and error handling
    • Learn model registry patterns with MLflow or Weights & Biases
    Resources
    • NVIDIA Triton Inference Server documentation and quick-start guides
    • HuggingFace Text Generation Inference GitHub repository
    • Practical MLOps (Noah Gift)
    • FastAPI documentation for building inference APIs
    Milestone

    You can take a trained model, containerize it, deploy it behind a scalable inference API, and manage model versions through a registry.

  4. Production Reliability & Observability

    4 weeks
    Goals
    • Design and implement monitoring pipelines with Prometheus and Grafana for AI-specific metrics
    • Set up distributed tracing with OpenTelemetry and Jaeger across inference microservices
    • Build alerting rules for latency degradation, error rate spikes, GPU saturation, and data drift
    • Implement CI/CD pipelines with GitHub Actions for automated model testing and staged deployments
    Resources
    • Site Reliability Engineering (Google SRE book)
    • Prometheus: Up & Running (Julien Pivotto)
    • OpenTelemetry documentation
    • GitHub Actions for MLOps tutorials
    Milestone

    You can build a production-grade observability stack for an AI service, set up automated deployment pipelines, and respond to incidents with proper runbooks.

  5. Inference Optimization & GPU Performance

    4 weeks
    Goals
    • Apply model quantization (GPTQ, AWQ, INT8, FP8) and benchmark quality vs. performance trade-offs
    • Configure continuous batching, PagedAttention, and KV cache optimizations in vLLM
    • Profile GPU workloads with NVIDIA Nsight Systems and identify memory/compute bottlenecks
    • Implement tensor parallelism and pipeline parallelism for serving large models across multiple GPUs
    Resources
    • vLLM documentation and source code
    • NVIDIA Deep Learning Performance Guide
    • Quantization and Pruning papers (GPTQ, AWQ, SmoothQuant)
    • PyTorch Profiler documentation
    Milestone

    You can optimize a large model's inference throughput by 2-5x through quantization, batching strategies, and GPU-level profiling.

  6. LLM Runtime Specialization & FinOps

    4 weeks
    Goals
    • Architect multi-model serving platforms with request routing, priority queuing, and tenant isolation
    • Design cost-optimized GPU fleets using spot instances, bin-packing, and auto-scaling strategies
    • Implement advanced deployment patterns: canary releases, shadow traffic, A/B testing for model quality
    • Build expertise in emerging LLM serving techniques: speculative decoding, disaggregated serving, and MoE inference
    Resources
    • vLLM architecture deep-dives and blog posts
    • AWS FinOps and cost optimization whitepapers
    • Orca: A Distributed Serving System for Transformer-Based Generative Models (paper)
    • Speculative decoding and disaggregated inference research papers
    Milestone

    You can architect, deploy, and operate a production LLM serving platform handling millions of daily requests with optimized cost and strict SLA compliance.

Practice Projects

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

Deploy a Sentiment Analysis API with Docker and FastAPI

Beginner

Containerize a pre-trained HuggingFace sentiment analysis model, wrap it in a FastAPI inference server with input validation, deploy it with Docker Compose, and add basic Prometheus metrics. This builds foundational skills in model serving and containerization.

~15h
Docker containerizationPython API developmentModel loading and serving

GPU-Accelerated Multi-Model Serving Platform with Triton

Intermediate

Deploy 3-5 different models (text classification, image recognition, embeddings) on NVIDIA Triton Inference Server with dynamic batching, model versioning, and concurrent execution. Add Grafana dashboards for per-model metrics.

~35h
Triton Inference ServerMulti-model servingDynamic batching

Auto-Scaling LLM Inference Pipeline on Kubernetes

Intermediate

Deploy a large language model (e.g., Llama 2 7B) on a Kubernetes cluster with GPU support, configure HPA with custom GPU and latency metrics, implement request queuing, and test scaling behavior under variable load using Locust.

~40h
Kubernetes GPU schedulingAuto-scaling configurationLoad testing

LLM Quantization and Optimization Suite

Advanced

Benchmark a 70B parameter model across multiple quantization methods (FP16, INT8, GPTQ-4bit, AWQ-4bit) and serving frameworks (vLLM, TGI, TensorRT-LLM). Build an automated benchmarking pipeline that measures latency, throughput, cost-per-token, and quality metrics.

~50h
Model quantizationInference optimizationBenchmarking methodology

Multi-Region LLM Serving Architecture with Failover

Advanced

Design and implement a multi-region inference architecture using Kubernetes federation or global load balancing, with automated failover, model artifact replication via container registry mirroring, latency-based routing, and chaos engineering tests to validate resilience.

~60h
Multi-region architectureDisaster recoveryGlobal load balancing

Production AI Observability and Incident Response Platform

Intermediate

Build an end-to-end observability stack for an AI inference service including Prometheus + Grafana monitoring, OpenTelemetry distributed tracing, alerting with PagerDuty integration, data drift detection with Evidently, and automated runbooks for common incidents.

~45h
Observability stack designAlert engineeringDistributed tracing

CI/CD Pipeline for ML Model Deployment with Quality Gates

Intermediate

Build a complete GitHub Actions CI/CD pipeline that triggers on new model artifacts, runs automated quality tests (accuracy, latency, memory), builds and scans container images, deploys to staging with smoke tests, and supports canary rollout to production with automated rollback.

~35h
CI/CD designAutomated testingCanary deployments

GPU Cost Optimization and FinOps Dashboard

Advanced

Build a cost optimization system that tracks GPU utilization per model, implements request batching to maximize utilization, evaluates spot instance viability, generates cost-per-inference reports, and provides recommendations for right-sizing and auto-scaling policies.

~40h
FinOps for AICost monitoringSpot instance management

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