role-based roadmap · AI & ML
AI Engineer Roadmap
A structured path from Python fundamentals through LLMs, RAG, agents, evals, and production AI systems — covering everything needed to land a job as an AI Engineer.
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1. Stage 1: Programming & CS Foundations
Python Fundamentals
Python is the primary language for all AI engineering work.
Data Structures & Algorithms
Enables writing efficient code and passing technical interviews.
Git & Version Control
Required for collaborating and managing all AI projects.
Linux & Command Line Basics
Most AI workloads run on Linux servers and cloud instances.
2. Stage 2: Math & Machine Learning Core
Linear Algebra & Calculus for ML
Underpins how neural networks learn and transform data.
Statistics & Probability
Drives model evaluation, uncertainty, and data understanding.
Classical Machine Learning
Builds intuition for modeling before moving to deep learning.
Data Wrangling with Pandas & NumPy
Cleaning and preparing data is 80% of real AI work.
3. Stage 3: Deep Learning & Neural Networks
Neural Network Fundamentals
Transformers and LLMs are built on these core concepts.
PyTorch Fundamentals
Industry-standard framework for building and fine-tuning models.
Transformers & Attention Mechanisms
Attention is the architecture powering every modern LLM.
Hugging Face Ecosystem
Provides pre-trained models, datasets, and deployment tools.
4. Stage 4: LLM Engineering & Prompt Design
Prompt Engineering
Shapes LLM output quality and reliability for any application.
LLM APIs — OpenAI & Anthropic
Most production AI apps are built on top of API-served LLMs.
LangChain & LlamaIndex Frameworks
Accelerate building chains, agents, and data-connected LLM apps.
Fine-Tuning & PEFT Methods
Adapts pre-trained models to domain-specific tasks efficiently.
5. Stage 5: RAG, Agents & Advanced AI Patterns
Retrieval-Augmented Generation (RAG)
Grounds LLM answers in real data, reducing hallucination.
Vector Databases
Enables fast semantic search for embedding-based RAG pipelines.
AI Agents & Tool Use
Agents autonomously plan and act, expanding LLM capabilities.
Model Context Protocol (MCP)
Standardizes how agents connect to tools and external context.
Evals & Guardrails
Ensures LLM outputs are safe, accurate, and production-worthy.
6. Stage 6: Data Engineering & MLOps
SQL & Databases for AI
AI systems constantly read from and write to structured data.
Cloud Platforms — AWS / GCP / Azure
Production AI is deployed and scaled on cloud infrastructure.
Docker & Containerization
Containers make AI models portable and reproducible across environments.
MLflow & Experiment Tracking
Tracks experiments, models, and artifacts through the ML lifecycle.
CI/CD for AI Systems
Automates testing and deployment of models and AI pipelines.
7. Stage 7: Job Readiness & Production AI
System Design for AI Applications
Interviews and real roles require designing scalable AI architectures.
AI Safety, Ethics & Responsible AI
Employers require engineers to build safe and unbiased systems.
Portfolio Projects & Open Source
Demonstrable work is the top signal for AI engineering hires.
Technical Interview Prep for AI Roles
Coding, ML theory, and system design rounds are all required.