Natural Language Processing

Understands the fundamentals of Natural Language Processing. Applies basic practices in daily work. Follows recommendations from the team and documentation.

Knows NLP basics: tokenization, stemming, NER, sentiment analysis. Understands how classic NLP tasks are solved with LLM and applies basic text preprocessing techniques.

Applies modern NLP methods: word embeddings (Word2Vec, FastText), sequence models (LSTM), pre-trained transformers (BERT, RuBERT). Solves tasks: NER, topic modeling, text summarization, semantic similarity. Fine-tunes BERT for domain-specific tasks.

Independently solves NLP tasks using LLM: text classification, NER, summarization, translation. Compares LLM approaches with classical methods, selects the optimal one for the task.

Designs production NLP systems with LLM integration. Develops RAG pipelines, semantic search, document understanding systems. Optimizes NLP models for production: distillation, quantization, efficient inference. Works with multilingual NLP.

Designs comprehensive NLP systems based on LLM: multi-task learning, zero-shot transfer, domain adaptation. Optimizes quality through prompt engineering, fine-tuning, and ensemble approaches.

Defines NLP strategy for the data science team. Establishes reusable NLP components and shared text processing infrastructure. Coordinates the choice between custom NLP models and LLM-based approaches for different tasks.

Defines NLP strategy for the LLM team. Establishes guidelines for approach selection (LLM vs classical NLP), evaluation methodology, domain adaptation strategies for various NLP tasks.

Shapes NLP and LLM strategy at organizational level. Defines investments in NLP infrastructure, evaluates build vs buy for NLP solutions. Shapes scientific and technical leadership in natural language processing.

Shapes enterprise NLP strategy based on LLM. Defines approaches to unified NLP platforms, multi-language support, and quality governance for NLP tasks at organizational scale.