5-Stage Learning Progression
Master each step sequentially before moving to advanced generative AI systems.
Python Mastery & Data Science Foundations
Master core Python 3.11+, OOP principles, asynchronous programming (`asyncio`), linear algebra, matrix operations, NumPy, Pandas, and Data Wrangling.
Machine Learning & Deep Learning Frameworks
Understand supervised & unsupervised ML, loss functions, gradient descent, neural network architectures, and hands-on tensor manipulation using PyTorch.
Transformers & Large Language Models (LLMs)
Deep dive into the Transformer architecture, multi-head self-attention mechanisms, HuggingFace Transformers library, tokenization, and open-source models (Llama 3, Mistral, Qwen).
Retrieval-Augmented Generation (RAG) & Vector Databases
Build production enterprise RAG systems. Master document chunking strategies, dense embedding models, vector databases (ChromaDB, Pinecone, Qdrant), and agentic frameworks (LangChain, LlamaIndex, AutoGen).
Production LLMOps & High-Throughput Serving
Deploy models for scale. Master high-throughput serving engines like vLLM, TensorRT-LLM, model quantization (AWQ/GGUF), Docker containerization, and API endpoints.
Recommended Portfolio Projects
Build these 3 real-world projects to showcase your AI engineering skills to recruiters.
Enterprise PDF Document Q&A (RAG)
Upload multi-page PDFs, generate vector embeddings with HuggingFace, store in ChromaDB, and retrieve answers grounded in document context using Streamlit.
Autonomous Code Review & Refactoring Bot
Multi-agent bot using Claude 3.7 / DeepSeek R1 that pulls GitHub PRs, analyzes code syntax, detects security vulnerabilities, and posts automated inline reviews.
High-Throughput Local LLM Serving Cluster
Host a quantized 7B/14B model locally using vLLM and TensorRT-LLM, exposing OpenAI-compatible endpoints with Redis caching and Prometheus latency monitoring.