Innovative NLP Engineer

9 hours ago

Amrāvati, Maharashtra, India beBeeNLP Full time ₹ 80,00,000 - ₹ 1,50,00,000

Research Opportunities:

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At our organization, we are seeking a highly motivated NLP Research Engineer to join our team.

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The successful candidate will be responsible for developing and implementing novel LLM architectures, prompt engineering strategies, and LLM training techniques tailored to healthcare and oncology text.

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Key Responsibilities:

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  • Model Development & Prototyping: Rapidly prototype novel LLM architectures, adapter modules, and prompt-tuning strategies for tasks such as clinical note summarization, entity extraction, and question answering.\
  • Experimental Design & Analysis: Define evaluation protocols, run ablation studies, and analyze results to quantify improvements in accuracy, robustness, and domain generalization.\
  • Proof-of-Concept Development: Build end-to-end POCs demonstrating how research breakthroughs translate into tangible gains on real healthcare datasets.\
  • Collaboration & Knowledge Sharing: Work alongside NLP engineers and MLOps teams to hand off production-ready prototypes; present findings in regular research seminars and contribute to internal or external publications.\
  • Literature & Trend Monitoring: Continuously survey the latest NLP/ML research, distill key innovations, and propose experiments to test applicability in our clinical environment.\
  • Bachelor's or Master's degree in Computer Science, Computational Linguistics, AI/ML, or a related field\
  • 2–3 years of professional R&D experience in NLP or ML research roles\
  • Strong Python skills and deep familiarity with PyTorch or TensorFlow\
  • 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.\
  • Expertise in prompt engineering, few-shot learning, and fine-tuning for domain-specific tasks\
  • Familiarity with building retrieval-augmented generation (RAG) pipelines.\
  • Hands-on experience with LLM fine-tuning using LoRA and SFT methods\
  • Solid understanding of experimental design, ablation studies, and statistical significance testing\
  • Familiarity with clinical or biomedical NLP is a plus\
  • If you have worked with Direct Preference Optimization (DPO), that's a plus\
  • Excellent communication skills for sharing insights in seminars, whitepapers, or publications\

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