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LLMOps / LLM DevOps Engineer End-to-end roadmap for deploying, scaling, and operating Large Language Models in production enterprise environments. Covers foundation model internals, prompt engineering, RAG pipelines, fine-tuning, model serving infrastructure, GPU orchestration, observability, cost optimization, security, and governance. Built for engineers who need to ship LLM-powered products that are reliable, compliant, and cost-effective at scale.
12 milestones in this roadmap
Step 1 beginner 3-4 weeks
LLM Foundations & Transformer Internals Understand Transformer architecture, tokenization, training pipelines, and scaling laws that drive infrastructure decisions
1 Transformer Architecture: Self-Attention, Multi-Head Attention, Feed-Forward Layers 2 Tokenization: BPE, SentencePiece, tiktoken, vocabulary size tradeoffs 3 Decoder-Only vs Encoder-Decoder vs Mixture of Experts (MoE) 4 Training Pipeline: Pretraining, Supervised Fine-Tuning, RLHF, DPO 5 Scaling Laws: Chinchilla Optimal, compute-data tradeoffs 6 Context Windows, KV-Cache, Sliding Window Attention 7 Model Families: GPT, LLaMA, Mistral, Claude, Gemini, Qwen
Step 1 beginner 3-4 weeks
LLM Foundations & Transformer Internals Understand Transformer architecture, tokenization, training pipelines, and scaling laws that drive infrastructure decisions
1 Transformer Architecture: Self-Attention, Multi-Head Attention, Feed-Forward Layers 2 Tokenization: BPE, SentencePiece, tiktoken, vocabulary size tradeoffs 3 Decoder-Only vs Encoder-Decoder vs Mixture of Experts (MoE) 4 Training Pipeline: Pretraining, Supervised Fine-Tuning, RLHF, DPO 5
Step 2 beginner 3-4 weeks
Prompt Engineering & LLM Application Patterns Master advanced prompting techniques, structured outputs, function calling, and LLM application design patterns
1 Zero-Shot, Few-Shot, Chain-of-Thought, Tree-of-Thought Prompting 2 ReAct, Self-Consistency, and Reflexion Patterns 3 Structured Output: JSON Mode, Function Calling, Tool Use 4 Prompt Templating, Versioning, and A/B Testing 5
Step 3 intermediate 5-6 weeks
RAG Pipelines - Enterprise Retrieval Augmented Generation Design and build production RAG pipelines with advanced retrieval, reranking, and evaluation
1 Document Ingestion: PDF, HTML, Confluence, Slack, S3 2 Chunking Strategies: Semantic, Recursive, Parent-Child, Late Chunking 3 Embedding Models: OpenAI, Cohere, BGE, E5, ColBERT 4 Vector Databases: Pinecone, Weaviate, Qdrant, pgvector, Milvus
Step 4 intermediate 5-6 weeks
Fine-Tuning, LoRA & Model Customization Master fine-tuning techniques including LoRA/QLoRA, dataset preparation, quantization, and experiment tracking
1 Full Fine-Tuning vs LoRA vs QLoRA vs DoRA 2 Dataset Preparation: Instruction Format, Alpaca, ShareGPT, DPO Pairs 3 Training: Single-GPU, Multi-GPU, DeepSpeed ZeRO, FSDP 4 Quantization: GPTQ, AWQ, GGUF, bitsandbytes (4-bit, 8-bit)
Step 5 advanced 5-6 weeks
Model Serving & Inference Infrastructure Deploy LLMs with production-grade inference engines, GPU optimization, batching, and autoscaling
1 Inference Engines: vLLM, TGI, TensorRT-LLM, Triton Inference Server 2 Continuous Batching and Dynamic Batching 3 KV-Cache Optimization: PagedAttention, Prefix Caching, Chunked Prefill 4 Speculative Decoding and Assisted Generation 5
Step 6 advanced 5-6 weeks
GPU Infrastructure & Kubernetes for LLMs Operate GPU clusters on Kubernetes with NVIDIA operators, multi-GPU scheduling, and cost-optimized infrastructure
1 GPU Fundamentals: CUDA, Tensor Cores, HBM2e/HBM3, NVLink, NVSwitch 2 GPU Selection: A100 vs H100 vs H200 vs L40S, Cost-Performance Analysis 3 Kubernetes GPU Scheduling: Device Plugin, GPU Operator, Time-Slicing 4 Multi-Instance GPU (MIG) and Multi-Process Service (MPS)
Step 7 intermediate 4-5 weeks
LLM Observability, Evaluation & Monitoring Implement LLM-specific observability with traces, evaluations, hallucination detection, and cost tracking
1 LLM Metrics: Tokens/sec, TTFT, ITL, Cache Hit Rate, Queue Depth 2 Distributed Tracing: Prompt -> Retrieval -> Generation Spans 3 LLM Evaluation: LLM-as-Judge, Reference-Based, G-Eval 4 Hallucination Detection and Factuality Scoring 5
Step 8 intermediate 4-5 weeks
CI/CD Pipelines for LLM Applications Build LLM-specific CI/CD with prompt regression testing, eval gates, model registries, and automated deployments
1 LLM CI/CD vs Traditional CI/CD: Key Differences 2 Prompt Regression Testing: Automated Eval Suites in CI 3 Eval Gates: Block Deployment on Quality Regression 4 Model Artifact Management: Registries, Checksums, Versioned Weights 5
Step 9 advanced 4-5 weeks
LLM Security, Red Teaming & Guardrails Secure LLM systems against prompt injection, data leakage, and adversarial attacks with guardrails and red teaming
1 Prompt Injection: Direct, Indirect, Multi-Turn Attack Vectors 2 Jailbreaking Techniques and Defense Layers 3 PII Detection and Redaction in Prompts and Outputs 4 Output Guardrails: Content Filtering, Toxicity, Topic Restriction 5
Step 10 advanced 5-6 weeks
Agentic Systems & Multi-Agent Orchestration Build and deploy production agentic systems with tool use, memory, multi-agent orchestration, and failure handling
1 Agent Architectures: ReAct, Plan-and-Execute, LATS, Reflexion 2 Tool Integration: APIs, SQL, Code Execution, Browser, File System 3 Memory Systems: Short-Term, Long-Term, Episodic, Shared Memory 4 Multi-Agent Patterns: Supervisor, Hierarchical, Debate, Swarm
Step 11 intermediate 3-4 weeks
Cost Optimization & FinOps for LLMs Optimize LLM costs with model routing, caching, prompt optimization, and FinOps practices
1 Token-Level Cost Analysis: Input vs Output, Cached vs Uncached 2 Model Selection Matrix: Quality vs Cost vs Latency Tradeoffs 3 Prompt Optimization: Compression, Caching, Batched Requests 4 Semantic Caching: Embedding Similarity, TTL, Cache Invalidation 5
Step 12 advanced 4-5 weeks
Governance, Compliance & Enterprise LLM Platform Build an enterprise LLM platform with governance, compliance, audit trails, and organizational scalability
1 Model Governance: Approved Registry, Version Policies, Deprecation Workflows 2 Data Governance: Training Data Lineage, Data Residency, PII Policies 3 Compliance: SOC2, HIPAA, GDPR, EU AI Act for AI Systems 4 Audit Trails: Prompt Logging, Model Versioning, Output Recording Ready to start this journey? Browse our courses and books to begin your learning path.
Hugging Face Transformers tiktoken PyTorch Jupyter Notebook Andrej Karpathy nanoGPT
Scaling Laws: Chinchilla Optimal, compute-data tradeoffs
6 Context Windows, KV-Cache, Sliding Window Attention
7 Model Families: GPT, LLaMA, Mistral, Claude, Gemini, Qwen
Hugging Face Transformers tiktoken PyTorch Jupyter Notebook Andrej Karpathy nanoGPT
System Prompts, Guardrails, and Instruction Hierarchy
6 Application Patterns: RAG, Agents, Classifiers, Extractors
7 Prompt Injection Attacks and Defense Strategies
OpenAI API Anthropic API LangChain LlamaIndex Instructor Pydantic Jinja2
Step 2 beginner 3-4 weeks
Prompt Engineering & LLM Application Patterns Master advanced prompting techniques, structured outputs, function calling, and LLM application design patterns
1 Zero-Shot, Few-Shot, Chain-of-Thought, Tree-of-Thought Prompting 2 ReAct, Self-Consistency, and Reflexion Patterns 3 Structured Output: JSON Mode, Function Calling, Tool Use 4 Prompt Templating, Versioning, and A/B Testing 5 System Prompts, Guardrails, and Instruction Hierarchy 6 Application Patterns: RAG, Agents, Classifiers, Extractors 7 Prompt Injection Attacks and Defense Strategies
OpenAI API Anthropic API LangChain LlamaIndex Instructor Pydantic Jinja2
5
Hybrid Search: Dense + Sparse + BM25 Fusion
6 Reranking: Cross-Encoders, Cohere Rerank, FlashRank
7 Query Transformation: HyDE, Multi-Query, Step-Back Prompting
8 Evaluation: RAGAS, Faithfulness, Answer Relevance, Context Precision
9 Multi-Tenant RAG with Row-Level Access Control
LangChain LlamaIndex Pinecone Weaviate pgvector Cohere Unstructured.io RAGAS
Step 3 intermediate 5-6 weeks
RAG Pipelines - Enterprise Retrieval Augmented Generation Design and build production RAG pipelines with advanced retrieval, reranking, and evaluation
1 Document Ingestion: PDF, HTML, Confluence, Slack, S3 2 Chunking Strategies: Semantic, Recursive, Parent-Child, Late Chunking 3 Embedding Models: OpenAI, Cohere, BGE, E5, ColBERT 4 Vector Databases: Pinecone, Weaviate, Qdrant, pgvector, Milvus 5 Hybrid Search: Dense + Sparse + BM25 Fusion 6 Reranking: Cross-Encoders, Cohere Rerank, FlashRank 7 Query Transformation: HyDE, Multi-Query, Step-Back Prompting 8 Evaluation: RAGAS, Faithfulness, Answer Relevance, Context Precision 9 Multi-Tenant RAG with Row-Level Access Control
LangChain LlamaIndex Pinecone Weaviate pgvector Cohere Unstructured.io RAGAS
5
Model Merging: TIES, DARE, SLERP, Task Arithmetic
6 Evaluation: Perplexity, MMLU, HumanEval, MT-Bench, Custom Benchmarks
7 Experiment Tracking and Model Registry
8 Continual Pre-Training and Domain Adaptation
Hugging Face TRL PEFT Axolotl Unsloth DeepSpeed bitsandbytes Weights & Biases MLflow
Step 4 intermediate 5-6 weeks
Fine-Tuning, LoRA & Model Customization Master fine-tuning techniques including LoRA/QLoRA, dataset preparation, quantization, and experiment tracking
1 Full Fine-Tuning vs LoRA vs QLoRA vs DoRA 2 Dataset Preparation: Instruction Format, Alpaca, ShareGPT, DPO Pairs 3 Training: Single-GPU, Multi-GPU, DeepSpeed ZeRO, FSDP 4 Quantization: GPTQ, AWQ, GGUF, bitsandbytes (4-bit, 8-bit) 5 Model Merging: TIES, DARE, SLERP, Task Arithmetic 6 Evaluation: Perplexity, MMLU, HumanEval, MT-Bench, Custom Benchmarks 7 Experiment Tracking and Model Registry 8 Continual Pre-Training and Domain Adaptation
Hugging Face TRL PEFT Axolotl Unsloth DeepSpeed bitsandbytes Weights & Biases MLflow
Tensor Parallelism vs Pipeline Parallelism vs Expert Parallelism
6 GPU Memory Planning: Weights + KV-Cache + Activations Budget
7 Autoscaling: Request-Based, Queue-Depth, GPU Utilization Triggers
8 Multi-Model Serving and Model Routing
9 Canary Deployments and A/B Model Testing
vLLM TGI (Text Generation Inference) TensorRT-LLM NVIDIA Triton Ray Serve BentoML NGINX / Envoy Prometheus
Step 5 advanced 5-6 weeks
Model Serving & Inference Infrastructure Deploy LLMs with production-grade inference engines, GPU optimization, batching, and autoscaling
1 Inference Engines: vLLM, TGI, TensorRT-LLM, Triton Inference Server 2 Continuous Batching and Dynamic Batching 3 KV-Cache Optimization: PagedAttention, Prefix Caching, Chunked Prefill 4 Speculative Decoding and Assisted Generation 5 Tensor Parallelism vs Pipeline Parallelism vs Expert Parallelism 6 GPU Memory Planning: Weights + KV-Cache + Activations Budget 7 Autoscaling: Request-Based, Queue-Depth, GPU Utilization Triggers 8 Multi-Model Serving and Model Routing 9 Canary Deployments and A/B Model Testing
vLLM TGI (Text Generation Inference) TensorRT-LLM NVIDIA Triton Ray Serve BentoML NGINX / Envoy Prometheus
5
Node Pools, Taints, Tolerations for GPU Workload Isolation
6 Spot/Preemptible GPU Strategies and Fallback Policies
7 Model Weight Storage: S3, EFS, Shared NFS, PVC Caching
8 Cluster Networking: NCCL, GPUDirect RDMA, InfiniBand for Multi-Node Training
9 GPU Monitoring: Utilization, Memory, Thermals, Xid Errors
Kubernetes NVIDIA GPU Operator NVIDIA DCGM Helm Terraform AWS EKS / GKE / AKS RunPod / CoreWeave / Lambda Labs
Step 6 advanced 5-6 weeks
GPU Infrastructure & Kubernetes for LLMs Operate GPU clusters on Kubernetes with NVIDIA operators, multi-GPU scheduling, and cost-optimized infrastructure
1 GPU Fundamentals: CUDA, Tensor Cores, HBM2e/HBM3, NVLink, NVSwitch 2 GPU Selection: A100 vs H100 vs H200 vs L40S, Cost-Performance Analysis 3 Kubernetes GPU Scheduling: Device Plugin, GPU Operator, Time-Slicing 4 Multi-Instance GPU (MIG) and Multi-Process Service (MPS) 5 Node Pools, Taints, Tolerations for GPU Workload Isolation 6 Spot/Preemptible GPU Strategies and Fallback Policies 7 Model Weight Storage: S3, EFS, Shared NFS, PVC Caching 8 Cluster Networking: NCCL, GPUDirect RDMA, InfiniBand for Multi-Node Training 9 GPU Monitoring: Utilization, Memory, Thermals, Xid Errors
Kubernetes NVIDIA GPU Operator NVIDIA DCGM Helm Terraform AWS EKS / GKE / AKS RunPod / CoreWeave / Lambda Labs
Regression Testing Across Model Versions and Providers
6 Cost Tracking: Per Request, Per User, Per Feature Attribution
7 Embedding Drift and Output Quality Drift Detection
8 Continuous Evaluation Pipelines and Golden Datasets
9 Alerting: Quality Degradation, Latency Spikes, Cost Anomalies
LangSmith Langfuse Arize Phoenix OpenTelemetry Prometheus Grafana Datadog LLM Monitoring Weights & Biases
Step 7 intermediate 4-5 weeks
LLM Observability, Evaluation & Monitoring Implement LLM-specific observability with traces, evaluations, hallucination detection, and cost tracking
1 LLM Metrics: Tokens/sec, TTFT, ITL, Cache Hit Rate, Queue Depth 2 Distributed Tracing: Prompt -> Retrieval -> Generation Spans 3 LLM Evaluation: LLM-as-Judge, Reference-Based, G-Eval 4 Hallucination Detection and Factuality Scoring 5 Regression Testing Across Model Versions and Providers 6 Cost Tracking: Per Request, Per User, Per Feature Attribution 7 Embedding Drift and Output Quality Drift Detection 8 Continuous Evaluation Pipelines and Golden Datasets 9 Alerting: Quality Degradation, Latency Spikes, Cost Anomalies
LangSmith Langfuse Arize Phoenix OpenTelemetry Prometheus Grafana Datadog LLM Monitoring Weights & Biases
Blue-Green and Canary Deployments for Model Swaps
6 Infrastructure-as-Code for GPU Resources (Terraform, Pulumi)
7 GitOps for Prompt Templates and Model Configurations
8 Rollback Strategies: Instant Prompt vs Gradual Model Rollback
9 Feature Flags for Prompt and Model A/B Testing
GitHub Actions GitLab CI ArgoCD Terraform Pulumi Helm Docker MLflow DVC
Step 8 intermediate 4-5 weeks
CI/CD Pipelines for LLM Applications Build LLM-specific CI/CD with prompt regression testing, eval gates, model registries, and automated deployments
1 LLM CI/CD vs Traditional CI/CD: Key Differences 2 Prompt Regression Testing: Automated Eval Suites in CI 3 Eval Gates: Block Deployment on Quality Regression 4 Model Artifact Management: Registries, Checksums, Versioned Weights 5 Blue-Green and Canary Deployments for Model Swaps 6 Infrastructure-as-Code for GPU Resources (Terraform, Pulumi) 7 GitOps for Prompt Templates and Model Configurations 8 Rollback Strategies: Instant Prompt vs Gradual Model Rollback 9 Feature Flags for Prompt and Model A/B Testing
GitHub Actions GitLab CI ArgoCD Terraform Pulumi Helm Docker MLflow DVC
Input Validation, Token Limits, and Sanitization
6 Model Access Control: RBAC, Rate Limiting, Usage Quotas
7 Open-Source Model Supply Chain: Provenance, Weight Verification
8 Red Teaming Methodologies and Automated Adversarial Testing
9 OWASP Top 10 for LLM Applications
NVIDIA NeMo Guardrails Guardrails AI LLM Guard Presidio (PII) Rebuff Garak python-dotenv HashiCorp Vault
Step 9 advanced 4-5 weeks
LLM Security, Red Teaming & Guardrails Secure LLM systems against prompt injection, data leakage, and adversarial attacks with guardrails and red teaming
1 Prompt Injection: Direct, Indirect, Multi-Turn Attack Vectors 2 Jailbreaking Techniques and Defense Layers 3 PII Detection and Redaction in Prompts and Outputs 4 Output Guardrails: Content Filtering, Toxicity, Topic Restriction 5 Input Validation, Token Limits, and Sanitization 6 Model Access Control: RBAC, Rate Limiting, Usage Quotas 7 Open-Source Model Supply Chain: Provenance, Weight Verification 8 Red Teaming Methodologies and Automated Adversarial Testing 9 OWASP Top 10 for LLM Applications
NVIDIA NeMo Guardrails Guardrails AI LLM Guard Presidio (PII) Rebuff Garak python-dotenv HashiCorp Vault
5
Agent Observability: Step-Level Traces, Decision Audit Logs
6 Failure Handling: Retries, Fallbacks, Human-in-the-Loop Escalation
7 Sandboxing: Code Execution, Network Isolation, Resource Limits
8 Long-Running Agents: State Persistence, Checkpointing, Resumption
9 Cost Control: Budget Limits, Token Caps, Circuit Breakers
LangGraph CrewAI AutoGen OpenAI Assistants API Anthropic Tool Use E2B (Code Sandbox) Docker Redis
Step 10 advanced 5-6 weeks
Agentic Systems & Multi-Agent Orchestration Build and deploy production agentic systems with tool use, memory, multi-agent orchestration, and failure handling
1 Agent Architectures: ReAct, Plan-and-Execute, LATS, Reflexion 2 Tool Integration: APIs, SQL, Code Execution, Browser, File System 3 Memory Systems: Short-Term, Long-Term, Episodic, Shared Memory 4 Multi-Agent Patterns: Supervisor, Hierarchical, Debate, Swarm 5 Agent Observability: Step-Level Traces, Decision Audit Logs 6 Failure Handling: Retries, Fallbacks, Human-in-the-Loop Escalation 7 Sandboxing: Code Execution, Network Isolation, Resource Limits 8 Long-Running Agents: State Persistence, Checkpointing, Resumption 9 Cost Control: Budget Limits, Token Caps, Circuit Breakers
LangGraph CrewAI AutoGen OpenAI Assistants API Anthropic Tool Use E2B (Code Sandbox) Docker Redis
Model Routing: Cheap-First Cascading, Confidence-Based Escalation
6 Self-Hosted vs API Break-Even Analysis at Different Traffic Volumes
7 Spot GPU Strategies and Reserved Capacity Planning
8 Cost Dashboards: Per-Request, Per-User, Per-Feature Attribution
9 Chargeback Models for Enterprise Business Units
GPTCache Redis OpenAI Batch API AWS Spot Instances Grafana Prometheus Kubecost OpenCost
Step 11 intermediate 3-4 weeks
Cost Optimization & FinOps for LLMs Optimize LLM costs with model routing, caching, prompt optimization, and FinOps practices
1 Token-Level Cost Analysis: Input vs Output, Cached vs Uncached 2 Model Selection Matrix: Quality vs Cost vs Latency Tradeoffs 3 Prompt Optimization: Compression, Caching, Batched Requests 4 Semantic Caching: Embedding Similarity, TTL, Cache Invalidation 5 Model Routing: Cheap-First Cascading, Confidence-Based Escalation 6 Self-Hosted vs API Break-Even Analysis at Different Traffic Volumes 7 Spot GPU Strategies and Reserved Capacity Planning 8 Cost Dashboards: Per-Request, Per-User, Per-Feature Attribution 9 Chargeback Models for Enterprise Business Units
GPTCache Redis OpenAI Batch API AWS Spot Instances Grafana Prometheus Kubecost OpenCost
5
Multi-Region Deployment for Data Sovereignty Requirements
6 Platform Architecture: API Gateway, Model Router, Prompt Library
7 Self-Service Onboarding for Internal Teams and Business Units
8 SLA Management: Latency, Availability, Quality Guarantees
9 Incident Response: Hallucination Events, Data Leakage, Model Degradation
Kong / Apigee (API Gateway) Open Policy Agent (OPA) HashiCorp Vault Terraform AWS Organizations / Azure Landing Zones Backstage (Developer Portal) PagerDuty Confluence / Notion
Step 12 advanced 4-5 weeks
Governance, Compliance & Enterprise LLM Platform Build an enterprise LLM platform with governance, compliance, audit trails, and organizational scalability
1 Model Governance: Approved Registry, Version Policies, Deprecation Workflows 2 Data Governance: Training Data Lineage, Data Residency, PII Policies 3 Compliance: SOC2, HIPAA, GDPR, EU AI Act for AI Systems 4 Audit Trails: Prompt Logging, Model Versioning, Output Recording 5 Multi-Region Deployment for Data Sovereignty Requirements 6 Platform Architecture: API Gateway, Model Router, Prompt Library 7 Self-Service Onboarding for Internal Teams and Business Units 8 SLA Management: Latency, Availability, Quality Guarantees 9 Incident Response: Hallucination Events, Data Leakage, Model Degradation
Kong / Apigee (API Gateway) Open Policy Agent (OPA) HashiCorp Vault Terraform AWS Organizations / Azure Landing Zones Backstage (Developer Portal) PagerDuty Confluence / Notion