Description

You will design and implement generative AI models and fine-tune NLP/NLU models to solve complex language understanding tasks.

Responsibilities

• Develop and fine-tune transformer-based models like BERT and GPT for generation, sentiment analysis, and entity recognition.
• Preprocess and curate text data through cleaning, tokenization, and augmentation for model training.
• Design and implement control and sandboxing systems for AI research.
• Optimize model performance using NLP metrics for real-world applications.
• Maintain and contribute to bug bounty and responsible disclosure programs.

Required Skills

• 5+ years of experience in NLP/NLU with a focus on deep learning and open-source LLMs such as Llama 2 or Falcon.
• Proficiency in Python and frameworks including spaCy, NLTK, and Hugging Face Transformers.
• Hands-on experience with PyTorch and TensorFlow.
• Practical use of Langchain, Embedchain, and Vector Databases such as Pinecone, FAISS, Milvus, ChromaDB, or Vespa.
• Direct engineering experience with high-performance, large-scale ML systems.
• Hands-on MLOps experience and familiarity with the end-to-end CI/CD process.
• Experience with API standardization and MLOps automation.
• Familiarity with cloud computing platforms for model training and deployment.
• Strong understanding of vector stores and search algorithms.

Preferred Skills

• Research publications or contributions in NLP, NLU, or Generative AI.
• Experience with Conversational AI and Chatbots such as Kore.ai, Botpress, or Nuance.

Key Skills

Education

Any Graduate