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4 days ago
We are seeking an experienced NLP/LLM Engineer to design and implement production-ready NLP pipelines, fine-tune open-source LLMs, and integrate LLMs into real-time products.
- Design and implement NLP pipelines for classification, named entity recognition, summarization, sentiment analysis, and question answering (RAG).
- Preprocess and structure large-scale textual datasets using SpaCy, NLTK, or custom tokenizers.
This role involves the development of robust and efficient NLP solutions that can be integrated into various applications.
- Develop prompt strategies and evaluate prompt robustness; implement few/zero-shot approaches where relevant.
- Build retrieval + generation (RAG) pipelines with vector DBs (FAISS, Weaviate, Pinecone).
The ideal candidate will have hands-on experience with Python and its ecosystem, particularly with libraries such as Hugging Face Transformers and SpaCy.
Key Requirements:
- Strong Python skills and experience with SpaCy, Transformers (Hugging Face), and NLTK.
- Hands-on experience fine-tuning/training Transformer models (BERT, T5, GPT variants).
- Experience integrating LLM APIs and working with prompt engineering.
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