Overview: We are seeking a dynamic and skilled Extern to join our cutting-edge AI initiatives at Nemak. This role is pivotal in driving the integration of Large Language Models (LLMs) and developing robust backend and frontend systems, ultimately advancing our AI capabilities and enhancing our technology infrastructure. Qualifications: • Educational Background: Currently pursuing or holding a degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field. • Technical Skills: o Strong backend development skills using Python, Flask, and experience with API development.

o Experience in front-end development with Angular or similar frameworks, with a solid understanding of UI/UX principles. o Familiarity with data processing pipelines, database management (including vector databases). o Familiarity with cloud hosting environments platforms such as Azure. o Beneficial: Experience with libraries such as LangChain, Llama Index, Haystack, or similar tools used in LLM applications and data retrieval tasks.

o Beneficial: Proficiency in integrating LLMs, particularly OpenAI models (e.g., GPT-3.5, GPT-4). Key Responsibilities: Backend Development: o Develop and maintain backend systems using Python and Flask, ensuring seamless integration with LLMs. o Manage APIs, databases, and data processing pipelines to support AI-driven applications. o Address technical challenges related to data integrity, token limitations, and storage solutions for vector databases.

Frontend Development: o Develop and enhance frontend interfaces using Angular, focusing on creating intuitive and user-friendly web applications. o Ensure the frontend seamlessly interacts with backend services, providing users with real-time access to AI powered tools. o Incorporate feedback from users to continuously improve the UI/UX of deployed applications. System Deployment: o Ensure all deployed systems are secure, scalable, and fully integrated with Nemak’s IT infrastructure.

o Address any challenges related to platform migration, such as compatibility issues or data transfer processes. Support LLM Integration and Optimization: o Integrate LLMs into existing systems to enhance functionality, with a focus on OpenAI models like GPT-4. o Configure LLMs to handle specific use cases, particularly in the context of RAG documentation retrieval. o Experiment with different model configurations and retrieval methods to optimize performance for complex queries.