GenAI and LLM Engineer

1 month ago


Bengaluru, Karnataka, India Talent Divas Consulting Full time

Job Title: GenAI and LLM Engineer (PyTorch)

Job Summary:

We are seeking a highly skilled GenAI and LLM Engineer with expertise in PyTorch to join our AI research and development team. This role is ideal for someone passionate about advancing the capabilities of Generative AI, building and fine-tuning large language models (LLMs), and implementing solutions using PyTorch. The selected candidate will work alongside AI scientists and engineers to develop, optimize, and deploy state-of-the-art generative models that impact various applications such as conversational AI, content generation, and text analysis.

Key Responsibilities:

  • Develop and implement state-of-the-art LLM architectures using PyTorch for various NLP and generative tasks, including text generation, question answering, and language translation.
  • Fine-tune and optimize pre-trained language models, ensuring high-quality output while minimizing latency.
  • Collaborate with data scientists to preprocess, curate, and manage large-scale datasets required for training and fine-tuning generative models.
  • Conduct experiments to benchmark model performance and iterate based on findings, ensuring models meet accuracy and efficiency standards.
  • Work closely with cross-functional teams, including ML engineers, software developers, and product managers, to document model development, training, and deployment workflows.

Requirements:

  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
  • 3+ years of experience working with GenAI or LLMs and extensive hands-on experience with PyTorch.
  • Proficiency with PyTorch for developing, training, and fine-tuning large-scale neural networks.
  • Deep understanding of LLM architectures and generative models.
  • Experience with data engineering and preprocessing for large-scale model training.
  • Knowledge of deployment frameworks and cloud-based model deployment.


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