Responsibilities:
Design, develop, and deploy NLP models for various text processing tasks such as knowledge extractions, summarization, and entity recognition.
Build and fine-tune Retrieval-Augmented Generation (RAG) models to enhance content generation and question-answering systems.
Integrate and optimize large language models (LLMs) for efficient performance in production environments.
Collaborate with clients and deliver NLP-based solutions that drive business objectives.
Research and apply the latest advancements in NLP and RAG, keeping up with cutting-edge techniques.
Implement scalable and robust pipelines for data preprocessing, training, and inference.
Deal with data to ensure clean and efficient data pipelines for NLP models.
Document and present research findings, technical solutions, and best practices to stakeholders.
Required Skills:
Bachelor’s or master’s degree in computer science, Data Science, AI, or a related field.
3+ years of experience in machine learning, with a strong focus on NLP and RAG techniques.
Hands-on experience with popular NLP frameworks such as Hugging Face Transformers, spaCy, or NLTK.
Proficiency in Python and experience with machine learning libraries (TensorFlow, PyTorch, Scikit-learn).
Experience building and deploying RAG models and working with large-scale language models.
Solid understanding of neural networks, deep learning architectures, and retrieval systems.
Experience with cloud platforms (e.g., AWS, Azure, GCP) for deploying machine learning models.
Strong problem-solving skills and ability to work in a fast-paced environment.
Knowledge of MLOps practices for model deployment and monitoring.
Preferred Skills:
Familiarity with reinforcement learning or conversational AI systems.
Experience in fine-tuning LLMs and customizing them for specific use cases.