Step-by-Step Learning Stages
Foundations: Math & High-Performance Python
Focus on linear algebra, matrix multiplication, vector calculus, probability theory, and fluent scientific Python writing.
Deep Learning & PyTorch Framework
Build neural networks from scratch. Understand backpropagation, gradient descent, loss functions, activation functions, and GPU tensor acceleration.
Transformers, LLMs & Fine-Tuning
Master Self-Attention, Multi-Head Attention, BERT, GPT architectures, HuggingFace Transformers, and Parameter-Efficient Fine-Tuning (LoRA, QLoRA, PEFT).
Generative AI, RAG & Vector Search
Ground LLMs with private data using Retrieval-Augmented Generation (RAG). Build vector pipelines with Pinecone, Qdrant, Milvus, and ChromaDB.
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.