Roles & 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.
Required 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).
GenAI Skills and Tools: RAG, Quantization, LLM-fine tuning, Parameter Efficient Fine-tuning (PEFT) using LoRA/QLoRA, LangChain, LangGraph/AutoGen/CrewAI.
MLOps: Atleast one of MLFlow, DVC, Wandb, Airflow.
Strong knowledge of machine learning and deep learning concepts, including supervised and unsupervised learning methods.
Research experience with publication(s) 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.