Roadmap to Senior AI/ML Engineer (with LLMs + MLOps + Systems)
Python → NumPy → Pandas → Matplotlib → Scikit-learn → Data Cleaning & EDA → Stats & Probability → Linear Algebra → Calculus → ML Algorithms (Regression, Trees, SVMs, KNN, Clustering) → Deep Learning (ANN, CNN, RNN, LSTM, GANs) → PyTorch / TensorFlow → Transfer Learning → Fine-tuning → Hugging Face Transformers → LangChain / LlamaIndex → LLM Internals (Tokenization, Attention, BPE, KV Cache) → RAG Pipelines → Vector DBs (FAISS, Weaviate, Pinecone) → Prompt Engineering → Finetuning (QLoRA / LoRA / DPO) → Model Deployment (Flask / FastAPI / Triton / BentoML) → Model Serving (TorchServe / TGI / vLLM) → Quantization (INT8 / GPTQ / AWQ) → Distillation → MLOps Basics → Model Versioning (DVC, MLflow) → Experiment Tracking → CI/CD for ML → Containerization (Docker) → Infra with Terraform → Kubernetes + Kubeflow → GPU Scheduling → Monitoring (Prometheus, Grafana, Sentry) → Cloud (AWS/GCP/Azure) → IAM, Billing, Cost Optimization → Ethics in AI → Bias, Fairness, Explainability
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