amitshekhariitbhu/ai-engineer-roadmap
AI Engineer Roadmap - A step-by-step AI Engineering roadmap to become an AI Engineer, with a blog for every topic.
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README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
This roadmap is designed so that anyone can follow it in the given order, even without any prior background in AI. Let's get started. Before jumping into the details, we must know the six words that come up in every AI Engineering conversation: Learn about all six in one video: AI Engineering Explained: LLM, RAG, MCP, Agent, Fine-Tuning, Quantization Now, we know the big picture. In the next steps, we will learn each of these in depth, one concept at a time. In this step, we will learn what Machine Learning is, the different ways a machine can learn, and the basic terms we will keep using in every later step. In this step, we will learn how a neural network actually learns. We will understand the math behind gradient descent and backpropagation step by step, and the techniques that make training stable. In this step, we will learn what…
Read the full README ↗ · Preview checked 2026-09-26T12:31:45.999Z
Inside the original README — Document outline
- AI Engineer Roadmap
- Table of Contents
- Prepared and maintained by the Founder of Outcome School: Amit Shekhar
- Follow Amit Shekhar
- Follow Outcome School
- I teach at Outcome School
- How to Use This Roadmap
- Step 0: Must Know
- Step 1: Machine Learning Foundations
- Step 2: Deep Learning and Neural Networks
- Step 3: Generative AI and the Transformer Architecture
- Step 4: How LLMs Generate Text
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- Step 7: Training, Fine-Tuning, and Alignment
- Step 12: LLM Inference Engineering
- Step 13: Evaluation and Observability
- Step 14: AI Safety and Security
- What is Deep RL from Human Preferences? The Paper That Started RLHF
- How to Chunk Documents for RAG? Chunking Strategies Explained
- LLM Inference Optimization
- LLM Inference Optimization
- Prefill vs Decode: LLM Inference Optimization
- What is Prefill-Decode Disaggregation in LLM Inference?
- What is LLM Evaluation? Metrics, Benchmarks, and Methods Explained
- How to Evaluate AI Agents? Metrics, Methods, and Best Practices
Documentation belongs to its respective authors. Reported project/model license: Apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
What this repository does
AI Engineer Roadmap - A step-by-step AI Engineering roadmap to become an AI Engineer, with a blog for every topic.
Repository facts
- Owner
- amitshekhariitbhu
- Primary language
- Markdown
- Stars
- 207
- Forks
- 18
- Open issues + pull requests
- 0
- License
- Apache-2.0
- Archived
- No
- Default branch
- main
- Created
- 2026-09-20T15:02:38.000Z
- Last push
- 2026-09-20T15:08:42.000Z
Topics and intended use
Owner-supplied topics: ai-engineer, ai-engineer-roadmap, ai-engineering, ai-engineering-roadmap
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