Advanced NLP Engineer Position

2 weeks ago

Ghaziabad, Uttar Pradesh, India beBeeNlpResearch Full time ₹ 18,75,000 - ₹ 22,50,000
Job Title:

NLP Research Engineer

About the Role:

We are seeking a driven NLP engineer with experience in model development and prototyping.

In this R&D-focused role, you will explore and implement state-of-the-art LLM architectures tailored to healthcare and oncology text.

Your primary responsibility is to drive proof-of-concepts—from rapid iterations to rigorous experimental evaluations on clinical-text tasks.

You will collaborate closely to hand off scalable prototypes that demonstrate clear performance gains.

Key Responsibilities:
  1. Model Innovation & Prototyping: Rapidly prototype novel LLM architectures and prompt-tuning strategies for tasks such as clinical note summarization, entity extraction, and question answering.
  2. Experimental Design & Analysis: Define evaluation protocols, run ablation studies, and analyze results to quantify improvements in accuracy, robustness, and domain generalization.
  3. Proof-of-Concept Development: Build end-to-end POCs demonstrating how research breakthroughs translate into tangible gains on real healthcare datasets.
  4. 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.
  5. Literature & Trend Monitoring: Continuously survey the latest NLP/ML research, distill key innovations, and propose experiments to test applicability in our clinical environment.
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), 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.
  9. Familiarity with clinical or biomedical NLP is a plus.
  10. If you have worked with Direct Preference Optimization (DPO), that's a plus.
  11. Excellent communication skills for sharing insights in seminars, whitepapers, or publications.

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