Research Engineer in Natural Language Processing (NLP)

Job Summary

As a Research Engineer in NLP, you will design, develop, and implement advanced natural language processing algorithms and models to solve complex problems in domains such as language understanding and text generation. This role is crucial in collaborating with an interdisciplinary team of researchers and engineers to advance state-of-the-art NLP and create practical applications for the industry.

Responsibilities

• Research and Development: Design, develop, and implement novel NLP algorithms and models to achieve state-of-the-art performance on various NLP tasks.
• Algorithm Engineering: Implement NLP algorithms and models from theory to practice, ensuring their scalability, efficiency, and performance.
• Experimentation and Evaluation: Conduct thorough experiments and evaluations of NLP models to determine their effectiveness and identify areas for improvement.
• Model Optimization and Deployment: Optimize and deploy NLP models for production-ready use cases, ensuring their reliability and maintainability.
• Knowledge Sharing and Collaboration: Collaborate with the research team to share knowledge, best practices, and research results.
• Staying Up to Date: Stay current with the latest developments in NLP research and industry trends.
• Documentation and Communication: Write technical reports, papers, and documentation to communicate research results and best practices.
• Mentorship and Training: Mentor and train junior researchers and engineers to develop their NLP skills.

Qualifications

• Educational Qualification:

  • Ph.D. or M.S. from top Indian institutes (IITs, IIITs, IISc, etc.) in Computer Science or a related field (e.g., NLP, linguistics, artificial intelligence, cognitive science).

• Experience:

  • 2 - 4 years of industry experience.

• Mandatory Skills:

  • Strong background in NLP, including language understanding, text processing, and machine learning.
  • Proficiency in one or more programming languages (e.g., Python, Java, C++, R).
  • Familiarity with NLP frameworks and toolkits (e.g., PyTorch, TensorFlow, spaCy, NLTK).
  • Generative AI skills and tools: RAG, Quantization, LLM fine-tuning, Parameter Efficient Fine-tuning (PEFT) using LoRA/QLoRA, LangChain, LangGraph/AutoGen/CrewAI.
  • MLOps experience with tools such as MLFlow, DVC, Wandb, or Airflow.
  • Strong knowledge of machine learning and deep learning concepts, including supervised and unsupervised learning methods.
  • Research experience with publications in top-tier NLP conferences.
  • Excellent problem-solving and analytical skills, with a strong ability to design and implement solutions.
  • Strong communication and collaboration skills, with the ability to effectively communicate technical research and ideas to both technical and non-technical stakeholders.

Experience

• 2 - 4 years of relevant industry experience is required.