Responsibilities
- Design, fine-tune, and deploy Large Language Models (LLMs) for AI agent applications.
- Implement Retrieval-Augmented Generation (RAG) pipelines to enhance the performance of AI agents in retrieving and generating contextually accurate responses.
- Develop and optimize traditional OCR systems for document digitization and text extraction tasks.
- Integrate OCR outputs with LLM-based AI agents to create seamless workflows for information processing.
- Collaborate with cross-functional teams to identify use cases, gather requirements, and deliver tailored AI solutions.
- Conduct data preprocessing, feature engineering, and model evaluation to ensure robust performance.
- Stay updated on the latest advancements in LLMs, RAG frameworks, computer vision, and OCR technologies.
- Document methodologies, experiments, and results for internal knowledge sharing.
Requirements
- Master’s or Ph.D. in Computer Science, Data Science, Machine Learning, or a related field.
- Strong experience with LLM fine-tuning (e.g., GPT models, BERT) and familiarity with frameworks like Hugging Face or OpenAI APIs.
- Proficiency in traditional OCR techniques using tools such as PaddleOCR models.
- Strong programming skills in Python; familiarity with libraries like PyTorch, TensorFlow, Scikit-learn, or similar.
- Experience with cloud platforms (AWS, Azure, GCP) for deploying machine learning models at scale.
- Excellent problem-solving skills and the ability to work independently or as part of a team.
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