What you’ll do:

• Design and implement Natural Language Processing (NLP) algorithms, leveraging Large Language Models (LLM) for text analysis and comprehension.
• Lead the collection of diverse and relevant datasets, focusing on unstructured data sources. Develop robust preprocessing pipelines to enhance data quality for model training Create advanced models for text summarization, ensuring concise and coherent representations of large volumes of textual information.
• Utilize Hugging Face frameworks and other relevant tools to train and optimize machine learning models.
• Continuously refine models for improved performance and efficiency.
• Utilize Hugging Face frameworks and other relevant tools to train and optimize machine learning models.
• Continuously refine models for improved performance and efficiency.
• Implement and integrate ChatGPT or similar conversational AI models into applications, ensuring seamless communication and user interaction.

What makes you a great fit:

• B.E/ B.Tech / MCA in computer science or a related field
• 4+years of experience in ML Algorithms
• Proficient in NLP techniques and methodologies, with a strong foundation in LLM applications.
• Fine tuning of pre-trained models into downstream application
• Familiarity with Retrieval augmented generation (RAG) - with vector database and LLMs
• Extensive experience in collecting and handling unstructured data for machine learning purposes.
• Expertise in developing models for text summarization and natural language understanding.
• 2+year Hands on experience in Database and should have a keen understanding of data models and data warehouse concepts
• Should have good development experience in Python and AWS.
• Solid understanding of machine learning frameworks, particularly Hugging Face, for efficient model training and deployment.
• Ability to build and fine-tune conversational AI models, such as ChatGPT, for specific use cases.
• Strong analytical and problem-solving skills, with the ability to derive actionable insights from complex data sets.
• NLP techniques such as n-grams, embeddings ( word2vec, GloVe, BERT)