Machine Learning Engineer
2 weeks ago
CreateBytes is seeking a highly skilled Machine Learning Engineer to join our team. As a key member of our engineering team, you will be responsible for developing and implementing NLP models using transformers, LLMs, and Generative AI/prompt engineering.
Key Responsibilities:- Design and implement NLP models using cutting-edge technologies such as transformers and LLMs.
- Work with open-sourced LLMs such as Microsoft Phi, Mixtral, or Llama models to develop and fine-tune 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 to optimize model performance.
- 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.
- 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.
- 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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