Become an AI Engineer with hands-on, GPU-backed labs
From transformers to production-grade RAG agents. Hands-on, GPU-backed.
What you'll be able to build
Eight capabilities, previewed with the actual animations from each module.
Build a transformer from scratch. Attention, multi-head, residuals, and the full block, trained on real data.
Open this moduleAbout 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
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ReadReady to start?
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