AI Engineer Roadmap: LLMs, RAG and AI Agents
Follow this AI engineer roadmap from zero to job-ready: Python, ML basics, LLMs, RAG, AI agents, evaluation, and shipping production AI products.
0 / 16 modules
Your recommended path
Start at Programming and Data Foundations for AI, then work through the Foundation path in order. Do not skip to LLMs and RAG before you can write a clean Python function and reason about a confusion matrix.
- Budget 9 to 12 months of consistent study. Two to three hours a day is enough.
- Do every hands-on lab instead of just reading.
- You can skip these for now: training models from scratch, reinforcement learning, and multi-agent frameworks.
- You will be ready for junior AI engineer roles once you complete the required Capstone projects.
Your recommended path
Your coding and API skills transfer directly.
- Skim Math, Statistics, and Machine Learning Foundations and Deep Learning for the core intuitions, and come back to them later if you need more depth.
- Your real gap is everything from LLM Fundamentals onward, so spend most of your time there.
- Budget 3 to 5 months from this entry point.
Your recommended path
You already understand models. Your gap is shipping them as products.
- Skim Math, ML, and Deep Learning as revision, then slow down from LLM Fundamentals onward. This is genuinely new territory even for experienced ML practitioners.
- Pay close attention to AI Evaluation and Testing and Building and Deploying AI Applications.
- Budget 3 to 6 months from this entry point.
Your recommended path
You have probably called an LLM API and built a demo already.
- Use this roadmap as a gap check. Most self-taught AI builders are weakest in evaluation, agent reliability, safety, and production deployment, which is exactly the back half of this roadmap.
- The required Capstone projects turn your prototypes into portfolio evidence for real interviews.
Steps in this roadmap
Status Module Topics Learn Quiz Resources Save Python for AI Engineering 15 Read +1 Data Handling and SQL for AI Engineers 10 Read Git and GitHub 12 Read +1 Status Module Topics Learn Quiz Resources Save Math and Statistics for AI Engineers 7 Read +1 Machine Learning for AI Engineers 14 Read +1 Status Module Topics Learn Quiz Resources Save Deep Learning Foundations for AI Engineers 9 Read +2 Status Module Topics Learn Quiz Resources Save LLM Fundamentals, Model Selection, and Prompt Engineering 12 Read +1 Status Module Topics Learn Quiz Resources Save Embeddings, Vector Databases, and AI Data Pipelines 9 Read Status Module Topics Learn Quiz Resources Save RAG in Production: Build, Debug, Improve 13 Read Fine-Tuning LLMs (Lite) 12 Read +1 Builds on How AI Agents Work and Tool Calling
Status Module Topics Learn Quiz Resources Save AI Agents, Workflows, and Reliability Engineering 13 Read +2 Status Module Topics Learn Quiz Resources Save AI Evaluation and Testing 10 Read +1 Builds on Docker for Beginners: Build, Run and Ship Containers
Status Module Topics Learn Quiz Resources Save LLMOps and Production AI Systems 12 Read Status Module Topics Learn Quiz Resources Save AI Safety and Responsible AI 12 Read Status Module Topics Learn Quiz Resources Save Advanced AI Specialisations 8 Read Status Module Topics Learn Quiz Resources Save AI Engineer Capstone and Interview Preparation 9 Read
Final assessment
Complete every step in this roadmap to unlock the final assessment.
Salary outcomes
- Junior AI Engineer
- ₹6L - ₹15L
- AI Engineer
- ₹15L - ₹35L
- Senior AI Engineer
- ₹35L - ₹70L
- Lead / Staff AI Engineer
- ₹70L - ₹120L
Explore other roadmaps
Frequently asked questions
12 steps and 16 modules, in order: Beginner (Programming and Data Foundations for AI, Math, Statistics, and Machine Learning Foundations and Deep Learning and Neural Networks); Intermediate (LLM Fundamentals, Model Selection, and Prompt Engineering, Embeddings, Vector Databases, and AI Data Pipelines, Retrieval Augmented Generation and Fine-Tuning and AI Agents, Workflows, and Reliability Engineering); Advanced (AI Evaluation and Testing, Building and Deploying AI Applications, AI Safety, Ethics, and Responsible AI and Specialisation - Choose Your Direction); Capstone (Capstone - AI Engineer Portfolio).
Yes. Every step and module of the AI Engineer Roadmap: LLMs, RAG and AI Agents roadmap is free to read on DevOps Network with no paywall. A free account lets you track progress, save modules and take the quizzes.
Pick the learning path closest to your background (I'm New to Tech, I'm a Software Engineer Moving into AI, I'm an ML Practitioner Adding Engineering Skills and I'm Already Working on Production AI Systems) at the top of the page. The step list then shows only the steps on that path; "All steps" brings the rest back.
Work through the step's modules, then pass its quiz. The quiz unlocks once the required modules are done, and passing it marks the step complete. You can also practise any quiz without the lock in the practice hub.
Yes. A final assessment unlocks after you complete all 12 steps and tests everything across the roadmap.
Typical ranges listed for this path: Junior AI Engineer: ₹6L - ₹15L; AI Engineer: ₹15L - ₹35L; Senior AI Engineer: ₹35L - ₹70L; Lead / Staff AI Engineer: ₹70L - ₹120L. Actual offers vary by company, city and experience.