NLP Engineer
For developers who want to build language AI, from classical text processing to transformers and LLMs. You will train, adapt, evaluate, and deploy production NLP systems.
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01 Programming and Engineering Foundations Master the programming, data, and software engineering skills that production NLP work relies on. 02 Mathematics for NLP Build the math intuition behind embeddings, attention, training, and probabilistic language models. 03 Machine Learning Fundamentals Learn core ML algorithms, optimization, and evaluation methods before moving into neural NLP. 04 Text Processing and Classical NLP Clean, represent, and analyze text with rule-based and statistical NLP techniques. 05 Deep Learning for NLP Train neural networks for text, from word embeddings to recurrent models and attention. 06 Transformers and Large Language Models Understand how transformers work and how modern language model families differ. 07 Adapting LLMs: Fine-Tuning, RAG, and Agents Adapt LLMs to your tasks with fine-tuning, alignment, prompting, retrieval, and agentic workflows. 08 NLP Applications and Evaluation Apply NLP to real tasks and measure quality with the right metrics and benchmarks. 09 LLMOps and Deployment Serve, scale, monitor, and secure NLP models efficiently in production environments. 10 Capstone Projects and Career Demonstrate end-to-end NLP skills through portfolio projects and prepare for NLP engineering interviews. NLP Engineer For developers who want to build language AI, from classical text processing to transformers and LLMs. You will train, adapt, evaluate, and deploy production NLP systems. 10 STAGES · 49 TOPICS · 180 SUBTOPICS 1.1 Python Advanced Python OOP · Generators · Decorators · Memory Management Asynchronous Programming asyncio · Concurrency Data Libraries NumPy · Pandas · Polars 1.2 SQL and Data Engineering SQL for Data Extraction Joins · CTEs · Window Functions Data Pipelines Apache Spark · Airflow · dbt Data Formats JSONL · Parquet · Arrow 1.3 Software Engineering Practices Version Control Git · GitHub · GitLab Testing pytest · Mocking · Integration Tests CI/CD Pipelines GitHub Actions · GitLab CI · Jenkins API Development FastAPI · gRPC · WebSockets 1.4 Data Structures and Algorithms Arrays, Hash Maps, and Tries Trees and Graphs Dynamic Programming Edit Distance · Viterbi Complexity Analysis 1.5 Systems Languages (Optional) C++ for Inference Engines and Kernels Rust for Tokenizers and Tooling 2.1 Linear Algebra Vectors, Matrices, and Tensors Matrix Multiplication and Norms Eigenvalues and SVD 2.2 Calculus Derivatives and Partial Derivatives Chain Rule and Gradients Jacobians and Hessians 2.3 Probability and Statistics Probability Distributions Bayes' Theorem Hypothesis Testing and Significance Markov Chains 2.4 Information Theory Entropy and Cross-Entropy KL Divergence Mutual Information Perplexity 3.1 Supervised Learning Linear and Logistic Regression Support Vector Machines Tree Ensembles Random Forest · XGBoost · LightGBM 3.2 Unsupervised Learning Clustering K-Means · DBSCAN · HDBSCAN Dimensionality Reduction PCA · t-SNE · UMAP 3.3 Optimization Gradient Descent and SGD Adaptive Optimizers Adam · AdamW · RMSprop Learning Rate Schedules Warmup · Cosine Decay 3.4 Model Evaluation Cross-Validation and Data Splits Classification Metrics Precision · Recall · F1 · ROC-AUC Bias-Variance and Overfitting 4.1 Text Preprocessing Regular Expressions and String Handling Text Normalization Lowercasing · Unicode · Spell Checking Stemming and Lemmatization Stop Word Removal String Similarity Edit Distance · Jaccard · Cosine 4.2 Tokenization Word and Sentence Tokenization Subword Algorithms BPE · WordPiece · Unigram · Byte-Level BPE Tokenizer Libraries SentencePiece · tiktoken · HF Tokenizers 4.3 Text Representation Bag of Words and N-Grams TF-IDF BM25 Lexical Retrieval 4.4 Sequence Labeling and Parsing Part-of-Speech Tagging Named Entity Recognition (NER) Probabilistic Models HMM · CRF · Viterbi Chunking and Shallow Parsing Dependency and Constituency Parsing 4.5 Text Classification and Topic Modeling Text Classifiers Naive Bayes · Logistic Regression · SVM Topic Modeling LDA · NMF · BERTopic 4.6 Classical NLP Libraries NLTK spaCy Gensim scikit-learn Stanza 5.1 Neural Network Fundamentals Feedforward Networks (MLP) Backpropagation and Computational Graphs Activation Functions ReLU · GELU · Sigmoid · Tanh · SiLU Loss Functions Cross-Entropy · NLL · Contrastive · Focal · Triplet Regularization Dropout · Weight Decay · LayerNorm · BatchNorm 5.2 Deep Learning Frameworks PyTorch JAX and Flax TensorFlow and Keras Triton GPU Programming 5.3 Word Embeddings Word2Vec CBOW · Skip-Gram · Negative Sampling GloVe FastText Subword Embeddings Contextual Embeddings (ELMo) 5.4 Sequence Models 1D CNNs for Text Recurrent Neural Networks and BPTT LSTM and GRU Bidirectional RNNs (BiLSTM) 5.5 Seq2Seq and Attention Encoder-Decoder Architecture Attention Mechanisms Additive · Multiplicative Pointer Networks and Copy Mechanisms 6.1 Transformer Core Scaled Dot-Product Self-Attention Multi-Head Attention MHA · MQA · GQA Positional Encoding Absolute · Relative · RoPE · ALiBi Normalization Post-LN · Pre-LN · RMSNorm Gated Activations SwiGLU · GeGLU Encoder and Decoder Blocks 6.2 Efficient Attention and Long Context FlashAttention KV Caching and PagedAttention Long Context Ring Attention · Sliding Window · RoPE Scaling 6.3 Pre-training Essentials Training Objectives Causal LM · Masked LM · Span Corruption Scaling Laws Data Curation Deduplication · Filtering · Data Mixtures 6.4 Pre-trained Model Families Encoder-Only BERT · RoBERTa · DeBERTaV3 · ModernBERT Decoder-Only GPT · Llama · Mistral · Qwen · Gemma Encoder-Decoder T5 · BART · FLAN-T5 6.5 Emerging Architectures Mixture of Experts Mixtral · DeepSeek-V3 · Qwen3 MoE Small Language Models Phi-4 · Gemma 3 · Qwen3 State Space and Hybrid Models Mamba · Jamba · RWKV Reasoning Models Test-Time Compute · Long Chain-of-Thought 7.1 Supervised Fine-Tuning Task-Specific Fine-Tuning Instruction Tuning (SFT) Fine-Tuning Tooling Hugging Face TRL · Unsloth · Axolotl · LLaMA-Factory 7.2 Parameter-Efficient Fine-Tuning LoRA and QLoRA LoRA Variants DoRA · PiSSA Adapters and Prompt Tuning Prefix Tuning · P-Tuning 7.3 Preference Alignment RLHF with PPO Direct Preference Methods DPO · KTO · ORPO RL for Reasoning GRPO · Verifiable Rewards Constitutional AI and RLAIF 7.4 Prompt Engineering Prompting Basics Zero-Shot · Few-Shot Reasoning Prompts Chain-of-Thought · Tree of Thoughts · ReAct Programmatic Prompt Optimization DSPy · TextGrad 7.5 Retrieval-Augmented Generation RAG Pipeline Chunking · Query Rewriting · Reranking Embedding Models BGE · E5 · Nomic · Jina · OpenAI Vector Databases FAISS · Qdrant · Milvus · pgvector · Pinecone Advanced RAG Hybrid Search · GraphRAG · Self-RAG RAG Frameworks LangChain · LlamaIndex · Haystack 7.6 Agentic Workflows Tool Use and Function Calling Model Context Protocol (MCP) Agent Frameworks LangGraph · CrewAI · OpenAI Agents SDK · Microsoft Agent Framework 8.1 Core NLP Applications Machine Translation Summarization Extractive · Abstractive Question Answering Extractive · Generative Sentiment and Emotion Analysis Document-Level · Aspect-Based Error Correction and Style Transfer Code Generation and Math Reasoning 8.2 Information Extraction Relation Extraction Coreference Resolution Knowledge Graph Construction Structured Extraction with LLMs 8.3 Conversational AI Dialogue Systems and Chatbots Intent Recognition and Slot Filling Dialogue State Tracking Conversational Frameworks Rasa · Botpress · Voiceflow 8.4 Multimodal NLP Vision-Language Models CLIP · LLaVA · Qwen-VL · Gemini · GPT-4o Speech and Audio Models Whisper · SeamlessM4T · Text-to-Speech Text-to-Image Generation Stable Diffusion · FLUX · Midjourney Video Understanding and Generation Gemini · Veo · Sora 8.5 Evaluation and Benchmarking Classic Metrics BLEU · ROUGE · METEOR · BERTScore LLM Benchmarks MMLU-Pro · GPQA · HumanEval · GSM8K · LMArena LLM-as-a-Judge and Reward Models RAG Evaluation Ragas · ARES · TruLens Evaluation Harnesses lm-evaluation-harness · HELM 9.1 Inference Optimization Numeric Precision FP16 · BF16 · FP8 · MXFP4 Quantization INT8 · INT4 · AWQ · GPTQ · GGUF Speculative Decoding Draft Models · Medusa · EAGLE Batching and Scheduling Continuous Batching · Chunked Prefill · Multi-LoRA 9.2 Serving Engines and Formats Inference Engines vLLM · SGLang · TensorRT-LLM Local and Edge Runtimes llama.cpp · Ollama · MLC-LLM · ExecuTorch Model Formats Safetensors · ONNX · GGUF 9.3 Cloud and Infrastructure Hugging Face Ecosystem Hub · Transformers · Datasets · Inference Endpoints Cloud ML Platforms SageMaker · Vertex AI · Azure ML GPU Clouds Modal · RunPod · Together AI · Baseten Containers and Orchestration Docker · Kubernetes · KServe · Ray Serve 9.4 Distributed Training Parallelism Strategies Data · Tensor · Pipeline · FSDP Training Frameworks DeepSpeed · Megatron-LM · Ray Train GPU Profiling Nsight Systems · PyTorch Profiler 9.5 Monitoring and Observability Quality Monitoring Data Drift · Concept Drift · Degradation Latency and Throughput TTFT · Tokens per Second Tracing and Tracking LangSmith · Langfuse · Phoenix · W&B · MLflow 9.6 Security and Responsible AI LLM Security Risks Prompt Injection · Jailbreaks · Data Leakage · Data Poisoning Guardrails NeMo Guardrails · Llama Guard Bias and Toxicity Mitigation Differential Privacy 10.1 Capstone Projects Domain-Specific NER or Classifier Data Labeling · Fine-Tuning · Evaluation Production RAG Assistant Hybrid Search · Reranking · Evaluation Fine-Tuned and Aligned Small LLM LoRA · DPO · Benchmarks Low-Latency LLM API vLLM · Quantization · Monitoring 10.2 Portfolio and Community Documented GitHub Projects Model and Dataset Cards Paper Reproductions and Blog Posts Open-Source Contributions 10.3 Interview Preparation NLP and LLM Fundamentals ML System Design for Language Apps Coding and Algorithms Research Paper Discussions