NLP Engineer

1 week ago


Bharatpur, Rajasthan, India beBeeNlpengineer Full time ₹ 1,80,00,000 - ₹ 2,50,00,000
Job Opportunity

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.
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.
  • 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).
Senior Roles:
  • 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).
Desirable Skills:
  • 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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