Large Scale NLP Model Architect

2 days ago

Hyderabad, Telangana, India beBeeNlp Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
Job Title

Lead Data Engineer for Large-Scale NLP Pipelines

Join a dynamic team in shaping the future of natural language processing. We are seeking an experienced Lead Data Engineer to spearhead the design and implementation of large-scale NLP pipelines for various tasks, including summarization, classification, and entity recognition.

  • Develop and optimize complex NLP models using domain-specific datasets and integrate them into live systems.
  • Build scalable and testable code for deploying AI models to production.
  • Collaborate closely with senior leadership to ensure technical alignment with product goals.
  • Contribute to AI/ML best practices and help standardize development workflows across teams.

Required Skills and Qualifications

  • Strong Python development skills with object-oriented programming principles and ML/AI best practices.
  • Expertise in machine learning, deep learning, and NLP using libraries like scikit-learn, PyTorch, and TensorFlow.
  • Practical experience with large language model fine-tuning, LoRA, PEFT, and embedding-based models.
  • Experience building retriever architectures for question-answering, search, and knowledge summarization.
  • Hands-on experience with vector stores (FAISS, Pinecone, ChromaDB), transformers, and Hugging Face models.
  • Experience deploying models via FastAPI, Flask, and Docker.
  • Good knowledge of speech-to-text tools like Whisper and AWS/GCP STT for transcription.
  • Familiarity with prompt engineering, LangChain, and retriever models.
  • Experience with cloud ML platforms (AWS, Azure, GCP) for model training and inference.
  • Exposure to MLOps practices like versioning, monitoring, and automated testing.

Benefits

  • Domain knowledge in financial services, marketplaces, healthcare, pharma, and life sciences NLP use cases.
  • Experience with knowledge graphs, graph neural networks, and embeddings-based reasoning.
  • Understanding of privacy-preserving ML (e.g., differential privacy, federated learning).
  • Exposure to LangChain, LlamaIndex, Haystack, or other GenAI orchestration tools.

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