What NLP Engineers Actually Build
NLP engineering spans from text preprocessing to production language AI systems.
Text Understanding Systems
Making machines comprehend language:
- Text classification — Spam detection, sentiment analysis, topic categorization
- Named entity recognition — Extracting people, places, organizations
- Intent detection — Understanding user queries and commands
- Semantic search — Finding relevant content by meaning
- Question answering — Extracting answers from documents
Text Generation Systems
Creating human-like text:
- Chatbots and assistants — Conversational AI systems
- Content generation — Summaries, descriptions, reports
- Machine translation — Language-to-language conversion
- Code generation — Converting natural language to code
- Text completion — Autocomplete and suggestion systems
NLP Infrastructure
Supporting language AI at scale:
- Data pipelines — Text preprocessing, tokenization, cleaning
- Model training — Fine-tuning transformers, custom models
- Evaluation systems — Metrics, A/B testing, quality measurement
- Serving infrastructure — Low-latency inference, caching
- Annotation tools — Labeling interfaces, quality control
Modern NLP Tech Stack (2024-2026)
Models and Frameworks
| Technology | Use Case |
|---|---|
| Transformers (Hugging Face) | Model hub, fine-tuning |
| OpenAI API | GPT-4, embeddings |
| LangChain | LLM applications |
| spaCy | Production NLP pipelines |
| NLTK | Classical NLP, preprocessing |
Infrastructure
- Training: PyTorch, TensorFlow, JAX
- Serving: vLLM, TensorRT, Triton
- Vector databases: Pinecone, Weaviate, Qdrant
- Evaluation: Weights & Biases, MLflow
Skills by Experience Level
Junior NLP Engineer (0-2 years)
Capabilities:
- Use pre-trained models effectively
- Implement basic text preprocessing
- Fine-tune models for specific tasks
- Evaluate model performance
- Work with NLP libraries
Learning areas:
- Custom model architectures
- Large-scale training
- Production optimization
- Advanced techniques
Mid-Level NLP Engineer (2-5 years)
Capabilities:
- Design end-to-end NLP systems
- Fine-tune and optimize transformers
- Build evaluation frameworks
- Handle production deployment
- Work with large datasets
- Mentor juniors
Growing toward:
- Architecture decisions
- Research implementation
- Technical leadership
Senior NLP Engineer (5+ years)
Capabilities:
- Architect complex NLP systems
- Lead model development strategy
- Optimize for scale and latency
- Bridge research and production
- Mentor teams
- Drive technical direction
Curiosity & fundamentals
Independence & ownership
Architecture & leadership
Strategy & org impact
Interview Focus Areas
Technical Fundamentals
- "Explain how transformer attention works"
- "How do you handle out-of-vocabulary words?"
- "What's the difference between BERT and GPT architectures?"
- "How do you evaluate a text classification model?"
Applied NLP
- "Design a semantic search system for e-commerce"
- "How would you build a content moderation system?"
- "How do you handle multilingual NLP?"
- "Walk me through fine-tuning a model for your domain"
Production Skills
- "How do you reduce inference latency for an NLP model?"
- "How do you handle model updates in production?"
- "What's your approach to prompt engineering?"
- "How do you measure model quality over time?"
Common Hiring Mistakes
Hiring Generic ML Engineers
NLP has unique challenges: tokenization, embeddings, linguistic features, evaluation metrics. Generic ML engineers need ramp-up time. Prioritize candidates with actual NLP project experience.
Overvaluing Academic Research
Papers matter less than production experience. Can they ship? Do they understand latency, scale, and reliability? Research NLP and production NLP are different skills.
Ignoring Classical NLP
Modern LLMs are powerful but not always the answer. Sometimes rule-based systems or simpler models work better. Good NLP engineers know when to use what.
Underestimating Domain Complexity
NLP for medical text differs from social media NLP. Domain expertise accelerates impact. Evaluate for relevant domain experience or ability to learn quickly.
Recruiter's Cheat Sheet
Resume Green Flags
- Production NLP systems at scale
- Transformer/LLM fine-tuning experience
- Published work or open-source contributions
- Domain-specific NLP experience
- Both research and engineering skills
Resume Yellow Flags
- Only academic/research experience
- No production deployment experience
- Only worked with pre-trained APIs
- Cannot explain model choices
Technical Terms to Know
| Term | What It Means |
|---|---|
| Transformer | Modern neural architecture for NLP |
| BERT/GPT | Popular transformer model families |
| Fine-tuning | Adapting pre-trained models to tasks |
| Embeddings | Vector representations of text |
| Tokenization | Breaking text into model inputs |
| RAG | Retrieval-Augmented Generation |
| Prompt engineering | Designing inputs for LLMs |