RAG Specialist - Engineering Document Intelligence

This role focuses on leveraging Retrieval-Augmented Generation (RAG) techniques to enhance engineering document intelligence capabilities. The successful candidate will be responsible for designing, developing, and implementing RAG solutions to improve document understanding, information retrieval, and overall efficiency within engineering workflows.

Key Responsibilities:

Develop and implement RAG pipelines for engineering documents.
Evaluate and select appropriate retrieval and generation models.
Fine-tune models to optimize performance and accuracy.
Integrate RAG solutions with existing engineering tools and systems.
Monitor and maintain RAG systems, ensuring reliability and scalability.
Collaborate with engineering teams to understand their document intelligence needs.
Research and stay up-to-date with the latest advancements in RAG and related fields.

Requirements/Qualifications:

Experience with RAG techniques and architectures.
Proficiency in Python and relevant machine learning libraries (e.g., TensorFlow, PyTorch).
Experience with large language models (LLMs).
Strong understanding of information retrieval concepts.
Experience with vector databases and embedding techniques.
Excellent problem-solving and analytical skills.
Ability to work independently and as part of a team.

About the Company: CT Automotive is a leading provider of innovative solutions within the automotive sector. The company focuses on developing advanced technologies to improve vehicle performance, safety, and efficiency.