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1 week ago
We are seeking a highly skilled Natural Language Processing (NLP) engineer to design and implement production-ready NLP pipelines, fine-tune open-source Large Language Models (LLMs), and integrate LLMs into real-time applications. This role involves collaborating with cross-functional teams to build classification, named entity recognition, summarization, sentiment analysis, and question-answering systems - from data preprocessing to deployment and monitoring.
Responsibilities:- Design and develop NLP pipelines for classification, named entity recognition, summarization, sentiment analysis, and question-answering.
- Preprocess and structure large-scale textual datasets using popular NLP libraries such as SpaCy, NLTK, or custom tokenizers.
- Fine-tune and optimize open-source models (BERT, T5, GPT-style) using Hugging Face Transformers.
- Integrate LLM APIs (OpenAI, Anthropic/Claude, Cohere, Hugging Face) into real-time applications and microservices.
- 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).
- Deploy model endpoints (Flask/FastAPI/Streamlit), containerize with Docker, and collaborate on cloud deployment.
- Monitor model performance, data drift, and user feedback; iterate based on metrics and qualitative signals.
- 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.
- Familiarity with embeddings, attention, tokenization, and vector similarity (cosine).
- Experience with vector DBs such as FAISS, Weaviate, or Pinecone.
- Comfortable with Docker, Git, and REST API development (Flask/FastAPI).
- Practical knowledge of evaluation metrics (BLEU, ROUGE, perplexity, cosine similarity).
- Track record of shipping multiple production LLM/NLP systems end-to-end.
- Strong system design skills (latency/cost tradeoffs, multi-tenant inference).
- Mentoring/leadership experience and ownership of ML lifecycle (MLOps).
- Experience with LangChain, LlamaIndex, PromptLayer, or similar LLMOps tooling.
- RAG workflows and building retrieval pipelines at scale.
- Experience in cloud deployment on AWS/GCP/Azure.
- Contributions to open-source projects or research publications.
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