Roadmap

AI Engineer

From math fundamentals to shipping production LLM and ML systems, the complete path to a job-ready AI Engineer.

18stages133topics~126hours

Curated from the best, MDN · Kubernetes · AWS · OWASP · Google SRE & more

AI Engineer is the fastest-growing technical role in 2025. The skill gap is enormous: most engineers can call an API; very few can evaluate quality, control cost at scale, or build reliable multi-agent systems. This path closes that gap end to end.

The complete path, 8 of 133 topics have lessons here; the other 125 are marked learn anywhere. We won't pretend we cover everything.

Start learning, it’s free Ready to build? See capstone projects

Stage 1 / 18 · 9 topics · 0 lessons

Programming & Engineering Foundations

The software engineering bedrock every AI Engineer needs before touching models.

Python CoreIdiomatic Python: data structures, comprehensions, generators, context managers, decorators. Python tutorial

Typing & Modern ToolingType hints, mypy/pyright, ruff, black, uv/poetry, virtual environments. uv docs

Async & Concurrencyasyncio, threads vs processes, GIL, concurrent I/O for high-throughput LLM calls. Python docs

Git & Version ControlBranching, rebasing, PR workflows, resolving conflicts in collaborative repos. Pro Git book

Data Structures & AlgorithmsComplexity analysis, hashing, trees, graphs, dynamic programming for interviews. GeeksforGeeks

Clean Code & Design PatternsrecSOLID, dependency injection, testable architecture, refactoring discipline. Refactoring Guru

Testing & TDDpytest, fixtures, mocking, property-based tests, coverage for ML pipelines. pytest docs

Linux & CLI FluencyrecShell, processes, file systems, SSH, scripting, package managers. The Linux Command Line

Secondary LanguagesoptionalReading Go/Rust/TypeScript for tooling, services, and frontend glue. TypeScript docs

Stage 2 / 18 · 6 topics · 0 lessons

Mathematical Foundations

The minimum viable math to reason about models, not just call them.

Linear AlgebraVectors, matrices, dot products, eigenvalues, matrix decomposition powering embeddings. Khan Academy

Calculus & GradientsDerivatives, chain rule, partial derivatives, the basis of backpropagation. Khan Academy

ProbabilityDistributions, conditional probability, Bayes, expectation, sampling. Khan Academy

Statistics & InferenceEstimation, hypothesis testing, confidence intervals, bias-variance. Khan Academy

OptimizationGradient descent, convexity, learning rates, loss landscapes. Deep Learning Book

Information TheoryrecEntropy, cross-entropy, KL divergence, perplexity, core LLM metrics. Wikipedia

Stage 3 / 18 · 8 topics · 0 lessons

Data Engineering & Manipulation

AI is data-bound; you must wrangle, store, and query it fluently.

NumPy & PandasVectorized arrays, dataframes, joins, groupby, cleaning at scale. NumPy docs

SQL & Relational DatabasesJoins, window functions, indexing, query optimization, schema design. PostgreSQL docs

Data Cleaning & EDAMissing values, outliers, normalization, exploratory visualization. pandas docs

Feature EngineeringEncoding, scaling, feature selection, leakage prevention. Google ML Crash Course

Data Pipelines & ETLrecAirflow/Dagster, batch vs streaming, dbt, orchestration patterns. Apache Airflow docs

Data Formats & StoragerecParquet, Arrow, JSON, data lakes, warehouses, object storage. YAML spec

Data Versioning & LabelingrecDVC, dataset cards, annotation workflows, label quality. DVC docs

Distributed Data ProcessingoptionalSpark, Ray Data, partitioning for large-scale preprocessing. Apache Spark docs

Stage 4 / 18 · 9 topics · 0 lessons

Classical Machine Learning

The fundamentals that frame every modeling decision, interview, and baseline.

ML Problem FramingSupervised vs unsupervised, train/val/test splits, the modeling lifecycle. Google ML

Linear & Logistic RegressionClosed-form vs gradient solutions, regularization, interpretation. Google ML Crash Course

Trees & EnsemblesDecision trees, random forests, gradient boosting (XGBoost/LightGBM). scikit-learn docs

Clustering & Dimensionality ReductionK-means, hierarchical clustering, PCA, t-SNE, UMAP. Google ML

Model EvaluationPrecision/recall, ROC-AUC, F1, cross-validation, calibration. scikit-learn docs

Overfitting & GeneralizationBias-variance tradeoff, regularization, early stopping. Google ML Crash Course

Imbalanced & Noisy DatarecResampling, class weights, threshold tuning, robust losses. imbalanced-learn docs

scikit-learn MasteryPipelines, transformers, grid search, the standard ML toolkit. scikit-learn docs

Model ExplainabilityrecSHAP, LIME, feature importance, partial dependence. SHAP docs

Stage 5 / 18 · 8 topics · 0 lessons

Deep Learning Foundations

Neural networks from first principles, the substrate of modern AI.

Neural Network BasicsPerceptrons, activations, forward pass, universal approximation. 3Blue1Brown

Backpropagation & TrainingGradient flow, loss functions, optimizers (SGD, Adam, AdamW). 3Blue1Brown

Regularization & NormalizationDropout, batch/layer norm, weight decay, data augmentation. Google ML Crash Course

PyTorchTensors, autograd, nn.Module, training loops, the dominant framework. PyTorch docs

CNNs & Computer VisionrecConvolutions, pooling, ResNets, transfer learning for images. CS231n

RNNs & Sequence ModelsoptionalLSTMs, GRUs, sequence modeling before transformers. colah's blog

Embeddings & RepresentationsWord2vec, learned representations, embedding spaces, similarity. Google ML Crash Course

GPU Training & Mixed PrecisionrecCUDA basics, fp16/bf16, gradient accumulation, multi-GPU. PyTorch docs

Stage 6 / 18 · 8 topics · 0 lessons

Transformers & LLM Internals

How the models you'll build on actually work under the hood.

Attention MechanismSelf-attention, query/key/value, scaled dot-product attention. Jay Alammar

Transformer ArchitectureMulti-head attention, positional encodings, encoder/decoder blocks. Jay Alammar

TokenizationBPE, WordPiece, SentencePiece, token economics, context windows. Hugging Face course

Pretraining ObjectivesrecCausal vs masked LM, next-token prediction, scaling laws. Hugging Face course

Sampling & DecodingTemperature, top-k, top-p, beam search, greedy decoding. Hugging Face docs

Model Families & LandscapeGPT, Claude, Llama, Gemini, Mistral, open vs closed tradeoffs. Hugging Face

Multimodal ModelsrecVision-language models, image/audio understanding, VLMs. Hugging Face docs

Emergent Abilities & LimitsrecIn-context learning, reasoning, hallucination, knowledge cutoffs. Anthropic Research

Stage 7 / 18 · 8 topics · 0 lessons

Prompt Engineering

Extracting reliable behavior from LLMs through structured prompting.

Prompting FundamentalsInstructions, roles, delimiters, zero-shot vs few-shot. Prompting Guide

Few-Shot & In-Context LearningExemplar selection, ordering effects, demonstration design. Prompting Guide

Chain-of-Thought & ReasoningStep-by-step prompting, self-consistency, reasoning models. Prompting Guide

Structured OutputJSON mode, schema enforcement, function/tool-call formatting. Claude docs

System Prompts & PersonasDesigning robust system prompts, guardrails, tone control. Claude docs

Prompt Chaining & DecompositionrecBreaking tasks into sub-prompts, intermediate verification. Claude docs

Prompt Optimization & VersioningrecSystematic iteration, A/B testing, DSPy, prompt management. Prompting Guide

Prompt Injection DefenseRecognizing and mitigating injection and jailbreak attacks. OWASP GenAI

Stage 8 / 18 · 7 topics · 1 lessons

Working with LLM APIs & SDKs

The practical layer of calling, controlling, and optimizing model providers.

Provider APIsOpenAI, Anthropic, Google, Bedrock, auth, params, message formats. Claude docs

Streaming ResponsesServer-sent events, token streaming, partial UI rendering. Claude docs

Tool / Function CallingDefining tools, parsing calls, executing and returning results. Claude docs

Prompt Caching & Cost ControlStart hereCaching, batching, token budgeting, model routing for cost.

Rate Limits & RetriesBackoff, concurrency control, quota management, failover. Claude docs

Local & Open-Model InferencerecOllama, vLLM, llama.cpp, serving open weights yourself. Ollama

Structured Output LibrariesrecInstructor, Pydantic, outlines for validated typed responses. Pydantic docs

Stage 9 / 18 · 9 topics · 0 lessons

Retrieval-Augmented Generation (RAG)

Grounding LLMs in your own data, the most common production AI pattern.

RAG FundamentalsWhy retrieval, the retrieve-augment-generate loop, when to use it. Prompting Guide

Embedding ModelsChoosing/evaluating embedding models, dimensions, domain fit. MTEB Leaderboard

Vector DatabasesPinecone, Weaviate, pgvector, Qdrant, indexing and ANN search. Pinecone Learn

Chunking & IndexingChunk size, overlap, semantic chunking, metadata enrichment. Pinecone Learn

Hybrid & Semantic SearchDense + sparse (BM25), fusion, filtering, query rewriting. Elastic

RerankingrecCross-encoder rerankers, relevance scoring, top-k refinement. Cohere docs

Advanced RAG PatternsrecMulti-query, HyDE, parent-document, contextual retrieval. LlamaIndex docs

GraphRAG & Knowledge GraphsoptionalEntity graphs, relationship-aware retrieval for complex corpora. Microsoft GraphRAG

RAG EvaluationFaithfulness, context relevance, RAGAS, retrieval metrics. Ragas docs

Stage 10 / 18 · 8 topics · 1 lessons

AI Agents & Orchestration

Building systems where LLMs plan, use tools, and act autonomously.

Agent FundamentalsReAct, planning, the perceive-reason-act loop, agent vs workflow. Anthropic

Tool Use & IntegrationsEquipping agents with APIs, code execution, search, databases. Claude docs

Agent MemoryShort-term context, long-term memory, summarization, state. LangChain docs

Multi-Agent SystemsrecOrchestrator-worker, role specialization, agent communication. LangChain docs

Agent FrameworksLangGraph, LlamaIndex, CrewAI, Agent SDKs, when to use each. LangGraph docs

Model Context Protocol (MCP)recStandardized tool/context servers for interoperable agents.

Agent Reliability & ControlLoop limits, error recovery, human-in-the-loop, guardrails. Anthropic

Computer & Browser UseoptionalAgents operating GUIs, browsers, and computer environments. Claude docs

Stage 11 / 18 · 7 topics · 1 lessons

Fine-Tuning & Model Adaptation

Customizing models when prompting and RAG aren't enough.

When to Fine-TunePrompt vs RAG vs fine-tune decision framework, cost/benefit. OpenAI docs

Supervised Fine-TuningInstruction tuning, dataset construction, training recipes. Hugging Face TRL docs

PEFT: LoRA & QLoRAParameter-efficient tuning, adapters, quantized fine-tuning.

Preference Tuning (RLHF/DPO)recReward models, RLHF, DPO/ORPO for alignment to preferences. Hugging Face TRL docs

Training Data CurationrecSynthetic data, distillation, quality filtering, deduplication. Hugging Face docs

Quantization & DistillationrecGPTQ/AWQ, int8/int4, model compression for cheaper serving. Hugging Face docs

Evaluating Fine-Tuned ModelsRegression testing, catastrophic forgetting, benchmark deltas. Hugging Face docs

Stage 12 / 18 · 7 topics · 0 lessons

Evaluation & Quality

You can't ship what you can't measure, rigorous eval is the senior skill.

LLM Eval FundamentalsOffline vs online, golden datasets, regression suites. Claude docs

LLM-as-JudgeModel-graded evals, rubrics, bias and reliability of judges. Hugging Face Cookbook

Benchmarks & LeaderboardsrecMMLU, HumanEval, MT-Bench, reading and their limitations. Hugging Face docs

Task-Specific MetricsrecBLEU/ROUGE, exact match, semantic similarity, pass@k. Hugging Face docs

Eval Frameworks & ToolingOpenAI Evals, LangSmith, Braintrust, promptfoo, DeepEval. Ragas docs

Human EvaluationrecAnnotation, inter-rater agreement, preference collection. Hugging Face docs

Hallucination & GroundednessDetecting fabrication, faithfulness checks, citation verification. Ragas docs

Stage 13 / 18 · 7 topics · 0 lessons

AI Application & System Design

Architecting real products around inherently probabilistic models.

LLM App ArchitectureOrchestration layers, caching, fallbacks, async pipelines. OpenAI Cookbook

Backend & API DesignFastAPI, REST, WebSockets, queues for AI workloads. FastAPI docs

Context Window ManagementCompaction, summarization, sliding windows, token budgeting. Claude docs

Latency & Cost ArchitectureModel routing, semantic caching, speculative decoding, batching. Claude docs

ML System Design InterviewsrecRecommenders, search, ranking, the FAANG interview format. Chip Huyen

Data Flywheels & FeedbackrecCapturing user signals to continuously improve the system. Chip Huyen

Graceful DegradationHandling timeouts, refusals, and failures without breaking UX. Azure Architecture Center

Stage 14 / 18 · 8 topics · 1 lessons

MLOps & LLMOps

Taking models from notebook to reliable, observable production.

Experiment TrackingMLflow, Weights & Biases, reproducible runs and artifacts. Weights & Biases docs

Model Registry & VersioningVersioning models, prompts, and datasets together. MLflow docs

Model Serving & InferencevLLM, TGI, Triton, KServe, throughput and autoscaling. vLLM docs

CI/CD for MLAutomated eval gates, model promotion, deployment pipelines. MLOps.org

LLM Observability & TracingLangSmith, Langfuse, Helicone, tracing, logging, debugging.

Monitoring & DriftQuality regression, data/concept drift, alerting on degradation. Evidently docs

Feature StoresoptionalFeast, online/offline parity for classical ML features. Feast docs

Online A/B TestingrecShadow deploys, canary, experiment-driven model rollout. Martin Fowler

Stage 15 / 18 · 7 topics · 4 lessons

Cloud, Infrastructure & Deployment

The platform skills to deploy and scale AI systems in production.

Containers & DockerImages, Dockerfiles, GPU containers, reproducible environments. Lab

KubernetesrecPods, deployments, autoscaling, GPU scheduling for inference. Lab

Cloud AI PlatformsAWS SageMaker/Bedrock, GCP Vertex, Azure AI services.

GPU & Accelerator InfrarecGPU types, provisioning, spot instances, cost optimization. NVIDIA docs

Infrastructure as CoderecTerraform, environment provisioning, repeatable deploys. Lab

Serverless & EdgeoptionalLambda, Modal, edge inference for lightweight workloads. AWS docs

Scaling & Load ManagementrecHorizontal scaling, queueing, backpressure, high availability. vLLM docs

Stage 16 / 18 · 7 topics · 0 lessons

AI Safety, Security & Guardrails

Shipping AI responsibly, the differentiator for production-grade engineers.

Guardrails & Content ModerationInput/output filtering, moderation APIs, policy enforcement. Claude docs

LLM Security (OWASP)Prompt injection, data exfiltration, the OWASP LLM Top 10. OWASP GenAI

PII & Data PrivacyRedaction, anonymization, data retention, consent handling. Microsoft Presidio

Red-Teaming & Adversarial TestingrecJailbreak probing, abuse testing, safety regression suites. Microsoft docs

Bias & FairnessrecMeasuring and mitigating model bias across groups. Google ML Crash Course

Hallucination MitigationGrounding, citations, confidence signals, abstention. Claude docs

Access Control & SecretsAPI key management, RBAC, tenant isolation, secure tool exec. Vault docs

Stage 17 / 18 · 4 topics · 0 lessons

Responsible AI & Governance

Legal, ethical, and compliance literacy expected of senior AI engineers.

AI Ethics & Responsible AIrecHarm analysis, transparency, accountability frameworks. Anthropic

AI Regulation & CompliancerecEU AI Act, NIST AI RMF, sector rules, model documentation. Google Cloud docs

Model & Data LicensingrecOpen-weight licenses, data provenance, IP and copyright. Hugging Face docs

Cost & Usage GovernanceoptionalBudgets, quotas, chargeback, monitoring spend across teams. Claude docs

Stage 18 / 18 · 6 topics · 0 lessons

Portfolio, Interviews & Career

Converting skills into a job offer at a top company.

Portfolio ProjectsShip 3-5 end-to-end AI apps demonstrating the full stack. Hugging Face docs

Coding InterviewsDSA, Python fluency, LeetCode-style problem solving. Tech Interview Handbook

ML/LLM System Design InterviewDesigning RAG, agents, and ML systems on the whiteboard. Chip Huyen

Applied ML & ML Theory RoundsrecModeling tradeoffs, metrics, debugging model behavior verbally. Chip Huyen

Staying CurrentrecReading papers, following releases, evaluating new tools fast. Hugging Face Papers

Communication & CollaborationrecWriting design docs, explaining tradeoffs to non-experts. Google SRE Book

You're job-ready.

Clear every stage, earn the certificate, and walk into interviews prepared. The complete path, nothing hidden, no gaps.

Destination reached