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AI / Generative AI Engineer Roadmap

Master AI engineering — the hottest role in tech right now. Learn to build applications with LLMs, fine-tune models, implement RAG, and deploy AI in production. Indian companies are hiring AI engineers at premium salaries.

5-8 months6-12 LPA → 35-70 LPA expected8 steps • 26 free resources
1

Python & ML Foundations

3-4 weeks

AI engineering requires solid Python and basic ML understanding. Master numpy, pandas, and core ML concepts before touching LLMs.

By the end, you'll be able to

  • Write production-quality Python code
  • Understand core ML concepts: training, inference, evaluation
  • Work with numpy, pandas, and scikit-learn
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Mini-project

Build a movie recommendation system with collaborative filtering. Use pandas for data processing and scikit-learn for the model.

2

Deep Learning & Neural Networks

3-4 weeks

Understand how modern AI works: neural networks, transformers, attention mechanism, and training dynamics.

By the end, you'll be able to

  • Build neural networks with PyTorch or TensorFlow
  • Understand the transformer architecture
  • Train and evaluate deep learning models
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Mini-project

Build a text classifier with PyTorch: data preprocessing, model training, evaluation, and inference pipeline.

3

LLMs & Prompt Engineering

2-3 weeks

Master Large Language Models: how they work, prompt engineering techniques, and how to get reliable outputs from ChatGPT, Claude, and open-source models.

By the end, you'll be able to

  • Write effective prompts for various use cases
  • Understand LLM architectures and limitations
  • Compare different LLMs for different tasks
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Mini-project

Build 5 prompt-engineered applications: a code reviewer, a content generator, a data extractor, a chatbot, and a summarizer.

4

LangChain & AI Application Framework

3-4 weeks

Build production AI applications. Learn LangChain/LlamaIndex for chaining LLM calls, tool use, agents, and complex AI workflows.

By the end, you'll be able to

  • Build AI applications with LangChain or LlamaIndex
  • Create AI agents that use tools and make decisions
  • Chain multiple LLM calls for complex workflows
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Mini-project

Build an AI research assistant that can search the web, read documents, answer questions, and cite sources.

5

RAG (Retrieval-Augmented Generation)

2-3 weeks

The most important pattern in enterprise AI. Learn to build RAG systems: vector databases, embeddings, chunking, and retrieval strategies.

By the end, you'll be able to

  • Build RAG pipelines with vector databases
  • Choose and configure embedding models
  • Implement effective chunking and retrieval strategies
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Mini-project

Build a RAG chatbot that answers questions about your company docs: PDF ingestion, embedding, vector store, and conversational UI.

6

Fine-Tuning & Model Customization

2-3 weeks

When prompts aren't enough, fine-tune. Learn LoRA, QLoRA, and how to customize models for specific domains and tasks.

By the end, you'll be able to

  • Fine-tune LLMs with LoRA and QLoRA
  • Prepare training datasets for fine-tuning
  • Evaluate fine-tuned models vs base models
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Mini-project

Fine-tune an open-source LLM for a specific domain (medical, legal, or financial). Compare against the base model.

7

AI in Production

2-3 weeks

Deploy AI to real users. Learn model serving, API design for AI, monitoring, cost optimization, and handling failure modes.

By the end, you'll be able to

  • Deploy AI models as APIs with proper error handling
  • Monitor AI systems: latency, quality, cost
  • Implement guardrails and safety measures
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Mini-project

Deploy your RAG chatbot to production: FastAPI backend, rate limiting, caching, monitoring, and a simple web UI.

8

Portfolio & Job Search

2-3 weeks

AI engineering is new — portfolios matter more than degrees. Build 3-5 impressive AI projects and showcase them.

By the end, you'll be able to

  • Have 3-5 deployed AI projects on GitHub
  • Write technical blog posts about your AI work
  • Articulate AI architectural decisions in interviews
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Mini-project

Build a portfolio with your best AI projects. Write a blog post about each. Apply to 20+ AI engineering roles.

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