LLM Finetuning
Posted Updated
Summary
ML/NLP engineer role focused on fine-tuning large language models for tasks like tokenization, embeddings and classification, using Python with TensorFlow, PyTorch and Transformers, in the financial lending domain (customer onboarding, loan servicing, collections). Open across multiple Indian cities.
Location: - Pune, Mumbai, Bangalore, Hyderabad, Chennai, Gurgaon,Mohali
Must have skills:
• Python Programming: Proficiency in Python,
• Working with libraries like TensorFlow, PyTorch,Transformers,NLTK,Pandas,sklearn, and other related libraries used in NLP tasks and fine tuning language models.
• Experience with building comprehensive python modules for NLP tasks like tokenization, word embeddings,classifications etc…
• Proficiency in data preprocessing techniques such as text cleaning, tokenization, normalization,handling of special characters is essential for preparing data for fine tuning language models.
• Knowledge of machine learning concepts like neural networks, optimization algorithms, model evaluation techniques.
• Deep learning expertise w.r.t NLP
• Expertise on adapting pre-trained language models to specific tasks and domains
• Experience with hyperparameter tuning methods such as grid search, random search, or Bayesian optimization for optimizing model performance during fine tuning.
• Experience in financial lending domain customer onboarding, loan servicing, collections.
• Expertise with tools and frameworks for handling large datasets.
• Ability to evaluate model performance using appropriate metrics and interpret model predictions
• Awareness of ethical considerations related to language models, such as bias mitigation, privacy preservation, and responsible AI practices • Ability to work with existing customer systems.
Must have skills:
• Python Programming: Proficiency in Python,
• Working with libraries like TensorFlow, PyTorch,Transformers,NLTK,Pandas,sklearn, and other related libraries used in NLP tasks and fine tuning language models.
• Experience with building comprehensive python modules for NLP tasks like tokenization, word embeddings,classifications etc…
• Proficiency in data preprocessing techniques such as text cleaning, tokenization, normalization,handling of special characters is essential for preparing data for fine tuning language models.
• Knowledge of machine learning concepts like neural networks, optimization algorithms, model evaluation techniques.
• Deep learning expertise w.r.t NLP
• Expertise on adapting pre-trained language models to specific tasks and domains
• Experience with hyperparameter tuning methods such as grid search, random search, or Bayesian optimization for optimizing model performance during fine tuning.
• Experience in financial lending domain customer onboarding, loan servicing, collections.
• Expertise with tools and frameworks for handling large datasets.
• Ability to evaluate model performance using appropriate metrics and interpret model predictions
• Awareness of ethical considerations related to language models, such as bias mitigation, privacy preservation, and responsible AI practices • Ability to work with existing customer systems.