Back
Mid-level, metro role with broad ML/NLP requirements increases applicant competition.
Highly specialized ML/NLP and LLM expertise limits cross-industry transfers.
Explicit years plus specific ML/LLM and infra skills raises filter strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Work on data science projects involving predictive modeling, natural language processing (NLP), and machine learning to extract insights and build data products.
Develop novel and scalable data systems integrating large datasets with machine learning techniques to enhance user experience.
Collaborate closely with product, development, and system architect teams to translate rich user data into actionable product improvements and innovations.
4.5+ years of work experience (Senior Software Engineer level).
Expertise in Python and practical experience with LLMs and retrieval-augmented generation (RAG) techniques.
Knowledge in machine learning/statistical algorithms including regression, neural networks, decision trees, clustering, deep learning, ensemble methods.
Experience with backend technologies (Flask/Gunicorn), natural language processing libraries (Spacy, NLTK, Gensim), deep learning frameworks (TensorFlow, PyTorch), and search technologies (Solr, OpenSearch, Elasticsearch).
Experienced in implementing multiple projects utilizing large language models (LLMs) and fine-tuning for search quality improvements.
Strong applied knowledge of search relevance technologies including embedding/vector search and prompt engineering frameworks (e.g., LangChain).
Proven ability to collaborate cross-functionally with engineering, product, and architecture teams in a fast-paced data science environment focused on NLP and machine learning.