Select your current position
Pick a role and level — we'll show the growth path, skills and gap analysis.
Development path
Junior
0-2 years
Responsibility: Completing tasks under the guidance of senior colleagues. Learning the codebase, standards, and team processes. Writing code to spec, fixing simple bugs, writing tests.
Key skills:
Middle
2-5 years
Responsibility: Independently developing features from decomposition to deployment. Participating in code review. Optimizing performance. Mentoring junior developers. Taking part in architecture discussions.
Key skills:
Senior
5-8 years
Responsibility: Designing the architecture of components and services. Solving complex technical problems. Managing technical debt. Code review as a quality gatekeeper. Mentoring middle developers. Choosing technologies for new tasks.
Key skills:
Lead / Staff
7-12 years
Responsibility: Technical leadership of a team or area. Designing system architecture. Coordinating with other teams. Establishing standards and best practices. Participating in hiring. Planning the technical roadmap.
Key skills:
Principal
10+ years
Responsibility: Technical strategy at the company or domain level. Cross-organizational influence. Solving systemic business problems through technology. Mentoring lead engineers. Publicly representing the company.
Key skills:
Gap analysis: skills to develop
To reach the next level you'll need to develop:
Independently integrates ChatGPT and Claude API into NLP pipelines. Configures system prompts for NER, sentiment analysis, text classification. Compares LLMs with fine-tuned models on quality and cost.
Independently develops NLP models with scikit-learn: text feature engineering, ensemble methods, hyperparameter tuning via GridSearchCV. Compares with deep learning approaches.
Independently configures Elasticsearch for NLP tasks: custom analyzers for multilingual text, mapping for NER annotations, aggregations for text analytics. Optimizes relevance via BM25 tuning.
Independently develops LLM applications for NLP tasks: chain-of-thought for complex NER, LLM-as-judge for text quality evaluation, structured output for document data extraction.
Independently designs ML pipelines for NLP tasks: data versioning, text feature engineering, hyperparameter tuning, model selection. Automates via Airflow or Prefect.
Independently manages MLflow for NLP projects: experiment organization, model registry, artifact store. Configures automatic logging from training scripts and comparison views.
Independently designs NLP model serving: TorchServe, Triton Inference Server. Configures batching, model versioning, A/B testing. Optimizes latency through model optimization.
Independently trains and fine-tunes NER models for domain-specific tasks. Annotates data, configures BIO/BILOU schemes, trains models on spaCy and Hugging Face transformers.
Independently processes large text datasets via pandas/Polars. Optimizes memory usage for corpora, applies vectorized string operations, integrates with NLP libraries.
Independently designs complex prompts for NLP tasks: structured output for data extraction, multi-step reasoning for document analysis, self-consistency for improving reliability.
Independently develops NLP models with PyTorch: fine-tuning transformers, custom loss functions for NLP tasks, data loaders for text corpora. Uses mixed precision training.
Independently designs RAG systems for NLP: hybrid search, reranking, query expansion. Configures chunking strategies for different document types, evaluates quality via RAGAS.
Independently fine-tunes transformer models for NLP: BERT, RoBERTa, T5 for domain-specific tasks. Configures tokenizers, training arguments, evaluation metrics via Hugging Face Trainer.
Independently trains sentiment models: fine-tuning BERT for domain-specific sentiment, aspect-based sentiment analysis, multi-class classification. Works with multilingual data.
Independently designs vector search for NLP: embedding model selection, index configuration, metadata filtering. Optimizes recall and latency for production semantic search.
Independently develops text classification systems: fine-tuning BERT/RoBERTa, zero-shot classification via LLM, multi-label classification. Works with imbalanced datasets.
Independently configures NLP model monitoring: data drift detection for text data, performance tracking, error analysis. Builds dashboards for tracking NLP service quality.
Independently organizes NLP model experiments: dataset versioning, configuration comparison, artifact tracking. Configures dashboards for monitoring training progress.