AI & Machine Learning Engineer
The complete, industry-standard learning journey from linear algebra and Python data science to PyTorch, Transformers, LLM fine-tuning, RAG, and production MLOps.
Orientation & AI Engineering Landscape
Understand modern AI stack, hardware accelerators (GPUs/TPUs), and algorithmic workflows. • ~2 weeks
Programming Foundations (Python & Vectorization)
Master Python data structures, memory model, and high-performance vector operations with NumPy. • ~4 weeks
Mathematics for Machine Learning
Linear algebra, matrix factorizations, gradients, multivariable calculus, and probability distributions. • ~6 weeks
Classical Machine Learning & Feature Engineering
Supervised & unsupervised algorithms, loss optimization, decision trees, and validation metrics. • ~6 weeks
Deep Learning & Neural Architectures
Backpropagation, autograd, PyTorch tensor mechanics, CNNs, and optimization algorithms. • ~8 weeks
Transformers, NLP & Generative AI
Self-attention mechanism, BERT, GPT decoder architectures, tokenization, and Hugging Face. • ~8 weeks
RAG, Vector Databases & Agents
Dense embeddings, chunking strategies, vector search (Qdrant/Pinecone), and agent tool-calling. • ~6 weeks
Production MLOps & Model Serving
High-throughput serving with vLLM/FastAPI, quantization (AWQ/GGUF), Docker, and evaluation pipelines. • ~6 weeks
AI Engineering Landscape & Tooling
Overview of generative models, compute layers (CUDA/ROCm), and workflow setup with Jupyter and VSCode.
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Connected career roadmaps, interview preparation, project ideas, and tutorials for this specialization.
Curated industry learning paths for Frontend, Backend, AI/ML, and DevOps Engineers.
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