Learning path·Intermediate · 40–60 hours

Become an AI Engineer with hands-on, GPU-backed labs

From transformers to production-grade RAG agents. Hands-on, GPU-backed.

115 interactive lessons49 coding challenges6 projects, auto-graded26 GPU labs
8
Modules
115
Interactive lessons
49
Coding challenges
26
Hands-on GPU labs
6
Build projects
3
Cert checkpoints
What you'll build
from text to agent
Fine-tune · LoRA adapter
W · frozen
+
×
A · B · trained
Fine-tune it on your data. LoRA adapts a frozen model cheaply.

What you'll be able to build

Eight capabilities, previewed with the actual animations from each module.

Self-Attention
The same token, rewritten by context
Sentence ①
I
sat
by
the
river
BANK
BANK · meaning vector
water
nature
place
money
finance
building

Build a transformer from scratch. Attention, multi-head, residuals, and the full block, trained on real data.

Open this module

About this path

An AI Engineer is the role that ships LLM-powered features to production: choosing the right model, fine-tuning when off-the-shelf isn't enough, building retrieval and tool-use loops, and running it all on GPUs that don't time out. This path teaches the role through real labs. Every concept ends in code that runs against a real GPU.

Skills you'll put on a resume

  • Implement a decoder-only transformer end-to-end and train it on real data
  • Fine-tune open-weight LLMs with LoRA and QLoRA on a single GPU
  • Build a production RAG pipeline with hybrid search, reranking, and grounded generation
  • Serve LLMs at production throughput with vLLM (PagedAttention, continuous batching, prefix caching)
  • Evaluate models with perplexity, LLM-as-judge, and preference data
  • Build production agents with ReAct, MCP tool servers, multi-agent orchestration, and memory
  • Pass the NVIDIA NCA-GENL, NCP-GENL, and NCP-AAI certifications

For

Software engineers comfortable with Python and basic ML who want to move into LLM/AI engineering work

Prerequisites

  • Comfortable Python (functions, classes, package management)
  • Familiarity with PyTorch or another ML framework
  • Basic ML literacy (training/eval, gradient descent, overfitting)

Guides & articles

Deep-dive reading that pairs with this course

NCA-GENL Complete Guide 2026 — NVIDIA Generative AI LLM Associate

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NCA-GENL Cheat Sheet 2026: Quick Reference for Exam Day

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How to Pass NCA-GENL on Your First Attempt (2026 Tips)

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NCA-GENL Exam Domains 2026: Weights, Topics & Study Strategy

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NCA-GENL 4-Week Study Plan: Week-by-Week Preparation Guide

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NCP-GENL Complete Guide 2026 — NVIDIA Generative AI LLM Professional

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NCP-GENL Exam Domains 2026: Weights, Topics & Study Strategy

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NCP-GENL 8-Week Study Plan: Week-by-Week Preparation Guide

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NCP-GENL vs NCA-GENL: Professional vs Associate — Which NVIDIA LLM Cert?

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NCP-GENL Model Optimization: Quantization, Pruning & TensorRT Guide

Master the NCP-GENL Model Optimization domain (17%). Covers quantization, pruning, distillation, TensorRT-LLM, and memory optimization for production LLMs.

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GPU Acceleration for NCP-GENL: Distributed Training & Parallelism Strategies

Master the NCP-GENL GPU Acceleration domain (14%). Covers data, model, tensor, and pipeline parallelism, DeepSpeed, Megatron-LM, and NVIDIA Nsight.

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NCP-GENL Fine-Tuning Guide: LoRA, QLoRA & PEFT for Production LLMs

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Best NCP-GENL Practice Tests 2026: Where to Prepare for the Exam

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What is NCP-AAI? NVIDIA Agentic AI Certification Explained

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NCP-AAI Exam Format 2026: Questions, Duration & What to Expect

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NCP-AAI Prerequisites: What You Need Before the Exam

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NCP-AAI vs AWS vs Google AI Certs: Which Should You Get?

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How Long to Prepare for NCP-AAI? Realistic Study Timeline

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NCP-AAI Cost & ROI: Is the $200 Exam Worth It in 2026?

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AI Agent Architecture Patterns: ReAct, Plan-Execute & More

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Multi-Agent Coordination: Orchestration Patterns for NCP-AAI

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NCP-AAI Practice Tests: Why You Need Them to Pass

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RAG for AI Agents: Retrieval-Augmented Generation NCP-AAI Guide

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NVIDIA NIM Deployment Guide: Docker, K8s & Cloud for AI Agents

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Tool Calling in AI Agents: NCP-AAI Function Integration Guide

Master tool use and function calling for the NVIDIA NCP-AAI exam. Covers parameter validation, error handling, tool selection strategies, and implementation patterns.

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AI Agent Memory Systems: Complete NCP-AAI Guide 2026

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Prompt Engineering for AI Agents: NCP-AAI Best Practices

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Agent Planning: ReAct vs CoT vs Tree of Thoughts (NCP-AAI)

Master agent planning strategies for the NVIDIA NCP-AAI exam. Covers ReAct, Chain-of-Thought, Tree of Thoughts, HTN planning, MCTS, A*, forward/backward planning, and when to use each approach.

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Building Production AI Agents: NCP-AAI Deployment Guide 2026

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ChromaDB vs Pinecone vs Weaviate: Vector DBs for AI Agents

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AI Agent Evaluation Metrics: CLASSic Framework & Benchmarks

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Agent Reasoning & Cognitive Architectures for NCP-AAI 2026

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AI Safety Guardrails: NeMo Guardrails for NCP-AAI Agents

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AI Agent Ethics & Compliance: GDPR, AI Act & NCP-AAI Guide

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AI Agent Monitoring: Observability & Alerting Best Practices

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Error Handling in AI Agents: Circuit Breakers, Retry & Recovery

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Testing AI Agents: Unit, Integration & Evaluation Strategies

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NVIDIA Riva for AI Agents: Speech Integration NCP-AAI Guide

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Agent State Management: Persistence Patterns for NCP-AAI

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NVIDIA NIM + LangChain: Production Integration for AI Agents

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Hugging Face for AI Agents: Transformers & NCP-AAI Guide

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NCP-AAI Cheat Sheet 2026: Quick Reference for Exam Day

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