End-to-End NLP & LLM Roadmap for ML Engineer Interviews

As an ML Engineer, I get asked the toughest questions on NLP, Generative AI, and LLMs.
Here’s my structured, end‑to‑end NLP roadmap to help you nail your next interview.

NLP Fundamentals

Lay the groundwork before diving into deep models:

• Tokenization

  • Word-level: splits on whitespace/punctuation (["The", "quick", "brown", "fox"])
  • Subword-level: BPE or SentencePiece handles OOV words ("unhappiness" → ["un", "##happi", "##ness"])
  • Sentence-level: for tasks like summarization or QA

• Text Cleaning & Normalization

  • Stopword removal (e.g., “the”, “is”) to reduce noise
  • Stemming (Porter, Snowball) vs Lemmatization (WordNet) for root forms
  • Lowercasing, removing URLs/HTML, handling emojis

• Linguistic Preprocessing

  • POS Tagging: e.g., ("runs", VERB) vs ("runs", NOUN)
  • Named Entity Recognition (NER): extract entities (PERSON, ORG, LOC)

• Bag of Words & TF‑IDF

  • Sparse vector representations: count vectors vs weighted TF‑IDF for importance

• Language Modeling Basics

  • n‑grams (unigram, bigram, trigram) and Markov chains for probability estimation
  • Naive Bayes for text classification: simple yet surprisingly effective baseline

Implement a custom tokenizer in Python to understand edge cases (hyphens, contractions).

Word Embeddings

Move from sparse to dense continuous representations:

• Word2Vec

  • CBOW (predict center word from context)
  • Skip‑Gram (predict context from center word)

• GloVe

  • Global co-occurrence matrix factorization—good for capturing global statistics

• FastText

  • Subword n‑grams improve representations for rare words

• Why Embeddings Matter

  • Capture semantic relationships: vec("king") - vec("man") + vec("woman") ≈ vec("queen")
  • Basis for downstream tasks—better initialization improves model convergence

Plot 2D t‑SNE of your trained embeddings to see clusters (e.g., countries, capitals).

Neural NLP

Sequence models that handle variable‑length text:

• RNN / LSTM / GRU

  • Vanilla RNNs suffer from vanishing gradients
  • LSTMs/GRUs introduce gates (input, forget, output) to manage long‑term dependencies

• Sequence‑to‑Sequence (Seq2Seq)

  • Encoder reads input sequence, decoder generates output—used in translation, summarization

• Attention Mechanism

  • Enables models to focus on relevant parts of the input when generating each token

• Encoder‑Decoder Framework

  • The foundation for many advanced architectures, including Transformers

Build a simple Seq2Seq chatbot using PyTorch’s nn.LSTM and attention to solidify concepts.

Transformers & BERT/GPT

The new standard for NLP:

• Transformer Architecture

  • Multi‑head self‑attention: parallel attention heads capture different relationships
  • Position encoding: injects order information via sin/cos functions

• BERT (Bidirectional Encoder)

  • Pre‑training: Masked Language Modeling (MLM) + Next Sentence Prediction (NSP)
  • Fine‑tuning: classification, NER, QA with task‑specific heads

• GPT (Causal Decoder)

  • Autoregressive next‑token prediction
  • Unidirectional attention for generation tasks

• Model Comparison

“Attention Is All You Need” (Vaswani et al.) and BERT’s original paper.

LLM Concepts You Must Know

Going beyond the Transformer:

• Pre‑training vs Fine‑tuning vs Prompting

  • Pre‑train on massive corpora; fine‑tune on task data; prompt at inference

• Prompt Engineering

  • Zero‑shot: no examples
  • Few‑shot: provide examples in prompt
  • Chain‑of‑Thought (CoT): guide model reasoning step by step

• PEFT (Parameter‑Efficient Fine‑Tuning)

  • LoRA, QLoRA, Adapters to fine‑tune only a fraction of parameters

• Instruction Tuning & RLHF

  • Align models with human preferences via reinforcement learning

• Retrieval‑Augmented Generation (RAG)

  • Combines embeddings + vector DB for context retrieval before generation

• Evaluation Metrics

  • BLEU, ROUGE for overlap; perplexity for language modeling; hallucination detection via QA checks

Compare vanilla vs PEFT‑fine‑tuned model performance on a custom text classification task.

GenAI in Production

From notebook to serving:

• APIs & SDKs

  • OpenAI, Hugging Face Inference API, Cohere for turnkey endpoints

• Orchestration Frameworks

  • LangChain, LlamaIndex to build RAG pipelines, chains, and agents

• Vector Databases

  • FAISS, Chroma, Weaviate, Pinecone for semantic search and retrieval

• Common Use‑Cases

  • Chatbots, document summarization, Q&A systems, semantic search

• Production Concerns

  • Prompt versioning: track changes & A/B test prompts
  • Latency: batching, caching, and async calls
  • Cost monitoring: token usage dashboards, budget alerts

Start with a simple RAG demo in Streamlit or Gradio, deploy on Vercel or AWS Lambda for real-world experience.

What Interviewers Really Want

Beyond theory, they look for:

• Intuition: can you explain why self‑attention works?
• Project Experience: live demos, GitHub repos, deployed apps
• Evaluation Awareness: know trade‑offs (speed vs accuracy), limitations (context length, biases), and metrics

Good luck in your AI/ML interviews!

Drop any questions or your own tips in the comments.