CreateBytes - Machine Learning Engineer - NLP/LLM

Job Description

Key Responsibilities :
 Develop and implement NLP models using transformers, LLMs, and Generative AI/prompt engineering (RAG architecture).
 Work with open-sourced LLMs such as Microsoft Phi, Mixtral, or Llama models.
 Utilize data science tools including Python scripting, CUDA, numpy, and more for efficient data handling and processing.
 Manage vector databases like ChromaDB and Pinecone.
 Employ libraries such as scikit-learn, Hugging Face, LangChain, LlamaIndex, PyTorch, Keras, etc., for model development and fine-tuning.
 Perform model fine-tuning on custom datasets, hyperparameter tuning, data collection, data preprocessing, ETL, and ELT.
 Execute prompt engineering to enhance AI model performance.
 Operate within cloud environments like Azure, GCP, or AWS and leverage ML frameworks like TensorFlow and PyTorch.
 Apply standard ML algorithms including supervised learning, unsupervised learning, and deep learning networks like RNNs and CNNs.
 Build and deploy RESTful APIs, serving them on virtual machines.

Qualifications
 Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
 Minimum of 2+ years of experience in NLP and machine learning.
 Proven experience with transformers, LLMs, and Generative AI.
 Strong proficiency in Python scripting, CUDA, numpy, and other data science tools.
 Knowledge and experience with vector databases (ChromaDB, Pinecone).
 Expertise in using ML libraries such as scikit-learn, Hugging Face, LangChain, LlamaIndex, PyTorch, and Keras.
 Skilled in model fine-tuning, hyperparameter tuning, and prompt engineering.
 Familiarity with cloud environments (Azure, GCP, AWS) and ML frameworks (TensorFlow, PyTorch).
 Solid understanding of standard ML algorithms and deep learning networks (RNNs, CNNs).
 Experience in building and deploying RESTful APIs on virtual machines.
 Excellent problem-solving skills and the ability to work in a collaborative team environment.

Preferred Qualifications
 Experience with open-sourced LLMs such as Microsoft Phi, Mixtral, or Llama models.
 Knowledge of advanced data preprocessing techniques and ETL/ELT processes.
 Strong understanding of AI ethics and data privacy considerations.

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