Specialises in language — search, extraction, classification, translation.
An NLP (Natural Language Processing) engineer specialises in systems that work with human language — search, text classification, named entity recognition, sentiment analysis, machine translation, and document understanding. This was a distinct specialisation before LLMs, and it remains one because many language problems require domain-specific solutions beyond general-purpose LLMs.
The role has shifted significantly. Before LLMs, NLP engineers trained custom models for each task. Now, much of the work involves fine-tuning or prompting LLMs, building retrieval systems, and handling the specific challenges of language data (tokenisation, multilingual support, domain-specific vocabulary).
Implement NLP pipelines using existing models and frameworks. Learn the Hugging Face ecosystem. Build evaluation pipelines for text tasks.
Design NLP systems end-to-end. Fine-tune models for domain-specific tasks. Own the text processing pipeline for a product area.
Architect language understanding systems. Make build-vs-buy decisions for NLP features. Lead cross-functional projects involving language data.
Define the language technology strategy. Evaluate new models and techniques. Influence product direction based on language AI capabilities.
Already working in another field? Here is how your background maps.
Learn NLP fundamentals (tokenisation, embeddings, transformers), then Hugging Face and the common NLP tasks. Build a text classification or search project to demonstrate skills.
Your understanding of language structure is genuinely valuable. Learn Python and the Hugging Face ecosystem. Focus on tasks like NER, text classification and multilingual NLP where linguistic knowledge gives you an edge.
You know Python, statistics and modelling. Learn NLP-specific concepts (tokenisation, transformers, embeddings) and the Hugging Face ecosystem. Many NLP tasks are classification problems you already understand.
