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AI Engineer Roadmaphttps://www.instagram.com/sambit.ai.tech ↗

Why it worked

The post provides a clear, structured, and visually appealing roadmap for a highly sought-after career path (AI Engineer), addressing a common pain point of 'what to learn next'. The actionable CTA encourages engagement.

Summary

This post outlines a comprehensive roadmap for aspiring AI Engineers, detailing the learning path from programming fundamentals to advanced AI topics and real-world product development. It emphasizes hands-on projects and practical application as key learning strategies.

Structure

1Introduction to the AI Engineer Roadmap
2Detailed breakdown of learning modules (Programming, Math, Data Analysis, ML, DL, CV, NLP, GenAI, Frameworks, MLOps)
3Emphasis on projects and practical application
4Advanced topics and final goal of building real-world AI products
5Call to action to comment 'ROADMAP' for more details

Product placement

The entire post is a roadmap for becoming an AI Engineer, presented by the creator @sambit.ai.tech, implying their courses or guidance are the subject.

Original caption

AI Engineer Roadmap (2026 Edition) 🚀 Everyone wants to become an AI Engineer. Very few know what to learn and in what order. Here’s a roadmap that takes you from Python → Machine Learning → Deep Learning → LLMs → AI Agents → MLOps → Real-world AI Products. 📌 Focus on building projects after every stage. 📌 Don’t get stuck watching tutorials forever. 📌 The fastest way to learn AI is to build. Save this roadmap and use it as your learning checklist. 💬 Comment ROADMAP if you want the detailed resources for each step. AI Engineer • Machine Learning • Deep Learning • NLP • Generative AI • AI Agents • RAG • LangChain • LangGraph • MLOps • Python • Data Science • Software Engineering • AI Career #AIEngineer #MachineLearning #GenerativeAI #SoftwareEngineer #aicareer

More from @sambit.ai.tech

On-screen text

follow @sambIT.AI.TECH AI ENGINEER ROADMAP 1 Programming Fundamentals (2-4 weeks) • Python Basics • Git & GitHub • OOPs Concepts • SQL Basics • Data Structures & Algorithms 2 Mathematics for AI (3-6 weeks) • Linear Algebra • Calculus Basics • Probability • Optimization Concepts • Statistics 3 Data Analysis (2-4 weeks) • NumPy • Pandas • Data Cleaning • Data Visualization (Matplotlib, Seaborn, Plotly) • Exploratory Data Analysis (EDA) 4 Machine Learning (6-8 weeks) Supervised Learning: • Linear Regression, Logistic Regression • Decision Trees, Random Forest, SVM Unsupervised Learning: • K-Means, PCA • Model Evaluation • Feature Engineering • Hyperparameter Tuning • Scikit-learn 5 Deep Learning (6-8 weeks) • Neural Networks • Activation Functions • Optimizers (SGD, Adam) • Forward & Backpropagation • Loss Functions • TensorFlow / PyTorch 6 Computer Vision (3-5 weeks) • OpenCV • Object Detection • CNNs • Segmentation • Image Classification • Transfer Learning 7 Natural Language Processing (NLP) (4-6 weeks) • Text Preprocessing • RNN, LSTM • Tokenization • Transformers • Word Embeddings • BERT • GPT Models 8 Generative AI (4-8 weeks) • LLM Fundamentals • Fine-Tuning • AI Agents • Prompt Engineering • Embeddings • Multi-Agent Systems • RAG • Vector Databases 9 AI Frameworks & Tools • LangChain • Ollama • LangGraph • OpenAI APIs • LlamaIndex • Gemini APIs • Hugging Face 10 MLOps & Deployment (4-6 weeks) • FastAPI • MLflow • Streamlit • Model Monitoring • CI/CD • Cloud (AWS / GCP / Azure) 11 Projects (Most Important) • Chatbot • Image Classification App • AI Agent • AI Resume Analyzer • Recommendation System • Multi-Agent Application • RAG Application • End-to-End AI Product 12 Advanced Topics • Fine-Tuning LLMs • AI Safety • LoRA & QLoRA • Model Quantization • Reinforcement Learning • Distributed Training FINAL GOAL Python → Math → Data Analysis → Machine Learning → Deep Learning → NLP → Generative AI → AI Agents → MLOps → Deployment → Real-world AI Products