Natural Language Processing Engineer

5 days ago

Kolkata, West Bengal, India beBeeNLP Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
Job Title: Natural Language Processing Engineer

This role is ideal for individuals who possess a deep understanding of NLP concepts and can develop novel LLM architectures, adapter modules, and prompt-tuning strategies.

We are seeking a highly skilled professional with experience in implementing state-of-the-art LLM techniques, including prompt engineering strategies and LLM training methods tailored to specific domains.

Your primary responsibility will be to drive proof-of-concepts from rapid iterations to rigorous experimental evaluations on clinical-text tasks. You will work closely with our production teams to hand off scalable prototypes that demonstrate clear performance gains.

Key Responsibilities:
  • Develop novel LLM architectures, adapter modules, and prompt-tuning strategies for tasks such as clinical note summarization, entity extraction, and question answering.
  • Define evaluation protocols, run ablation studies, and analyze results to quantify improvements in accuracy, robustness, and domain generalization.
  • Build end-to-end POCs demonstrating how research breakthroughs translate into tangible gains on real healthcare datasets.
  • Collaborate with NLP engineers and MLOps teams to hand off production-ready prototypes; present findings in regular research seminars and contribute to internal or external publications.
Requirements:
  1. Bachelor's or Master's degree in Computer Science, Computational Linguistics, AI/ML, or a related field.
  2. 2–3 years of professional R&D experience in NLP or ML research roles.
  3. Strong Python skills and deep familiarity with PyTorch or TensorFlow.
  4. Proficiency in working with NLP frameworks (e.g., spaCy, NLTK, HuggingFace Transformers, LangChain, vectorDBs, etc), deep learning libraries (e.g., PyTorch), and common data preprocessing techniques.
  5. Expertise in prompt engineering, few-shot learning, and fine-tuning for domain-specific tasks.
  6. Familiarity with building retrieval-augmented generation (RAG) pipelines.
  7. Hands-on experience with LLM fine-tuning using LoRA and SFT methods.
  8. Solid understanding of experimental design, ablation studies, and statistical significance testing.

The ideal candidate should have a strong foundation in NLP principles and the ability to apply them to complex problems. This role requires excellent communication and collaboration skills, as well as the ability to work independently on challenging projects.

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