Complete Roadmap to Become an AI Engineer (2025 Edition)

Phase 1:Mathematics and Programming Fundamentals

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Duration:

Topics Covered:

• Python for AI: numpy, pandas, matplotlib, seaborn
• Linear Algebra: vectors, matrices
• Probability and Statistics
• Calculus: gradients, optimization
• Basic Data Structures and Algorithms
• 
  

Recommended Resources:

• Mathematics for Machine Learning (Imperial College, Coursera)
• Python for Data Science Handbook by Jake VanderPlas
• Practice logic on LeetCode or HackerRank
• 
  

Mini Project Idea:

Phase 2:Machine Learning (Core AI Foundation)

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Duration:

2–3 months
Goal:

Understand and implement core ML algorithms from scratch.

Topics Covered:

• Supervised vs Unsupervised Learning
• Linear and Logistic Regression
• Decision Trees and Random Forest
• K-Means Clustering and PCA
• Model Evaluation: accuracy, precision, recall, AUC
• Overfitting and Regularization

Tools Used:
scikit-learn, numpy, pandas, matplotlib, seaborn

Project Ideas:

• Loan Default Prediction
• Customer Segmentation
• Spam Email Classifier
• 
  

Phase 3: Deep Learning and Neural Networks

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Duration:

2–3 months
Goal:

Learn how deep neural networks work and build real-world models.

Topics Covered:

• Neural Networks: forward and backpropagation
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs) and LSTMs
• Transfer Learning and Autoencoders
• Hyperparameter Tuning

Frameworks:
TensorFlow, Keras, PyTorch

Project Ideas:

• Cats vs Dogs Image Classifier
• Face Emotion Recognition
• MNIST Handwritten Digit Recognizer
• 
  

Phase 4: Natural Language Processing (NLP)

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Duration:

2 months
Goal:

Build AI systems that understand and generate human language.

Topics Covered:

• Tokenization, Stemming, Lemmatization
• Word Embeddings: Word2Vec, GloVe
• Transformers and Attention Mechanisms
• BERT and GPT Models
• Prompt Engineering for LLMs

Tools Used:
Hugging Face Transformers, OpenAI API, LangChain, LlamaIndex

Project Ideas:

• Restaurant Chatbot
• Resume Summarizer using GPT
• Sentiment Analysis for Reviews
• 
  

Phase 5:LLM Engineering and Agentic AI

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Duration:2–3 months

Goal:Learn to fine-tune and deploy Large Language Models (LLMs).

Topics Covered:

• APIs: OpenAI, Anthropic, Ollama
• Retrieval-Augmented Generation (RAG)
• Vector Databases: Pinecone, FAISS, Chroma
• Fine-tuning LLMs: Llama 3, Mistral
• Multi-agent Systems: CrewAI, LangGraph
• Tool Calling and Function Execution
• 
  

Project Ideas:

• LLM-powered Customer Support Agent
• Personal AI Assistant with Memory
• AI-based Food Ordering Chatbot
• 
  

Phase 6:AI Deployment and MLOps

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Duration:

1–2 months
Goal:

Learn to deploy AI models in production environments.

Topics Covered:

• Model Serving: FastAPI, Flask
• Docker and Kubernetes for ML
• CI/CD for AI Systems
• Monitoring: Drift Detection, Logging
• Cloud AI Services: GCP Vertex AI, AWS SageMaker, Azure AI Studio

Project Ideas:

• Sentiment Analysis API on GCP
• End-to-End ML Pipeline with Airflow and MLflow
• 
  

Phase 7: AI Specializations (Choose 1–2)

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Duration: Continuous

Goal:

Deepen expertise in a specific AI domain.

Bonus: Portfolio and Resume Building

• -Publish your projects on GitHub
• -Write technical articles on Medium or LinkedIn
• -Contribute to open-source AI repositories
• -Build a personal AI app or API as a showcase project
• 
  

Suggested Timeline Summary

Total Duration:
12–14 months (part-time)
6–8 months (full-time)