Artificial Intelligence Intern [IDA:00029]

Primary Goals:
- Develop an Agentic AI framework to standardize requirements expressed in natural language.
- Explore and implement methodologies to enhance the effectiveness and efficiency of requirement normalization using Agentic AI.
- Evaluate the performance of the developed model through rigorous testing and comparison with existing methods.
- Explore and implement methods to make open-source LLMs assist in requirement domain-specific generation, including data pre-processing, model-finetuning, etc.
- Document the project findings and present them in a clear and comprehensible manner.

This internship provides an excellent opportunity for talented individuals passionate about Artificial Intelligence, Generative AI, Agentic AI, and Prompt Tuning. As a part of our team, you will collaborate with experienced practitioners and researchers, gain practical experience in state-of-the-art technologies, and contribute to pioneering research in the field of Generative AI assisted by Agentic AI.

Activities:

- Research and understand the principles and applications of Generative AI, with a focus on Agentic AI.
- Design and implement an Agentic AI framework and Generative AI model capable of normalizing requirements expressed in various forms of natural language.
- Develop methods to assist and augment the knowledge retrieval from multiple sources and associated generation (Retrieval Augmented Generation).
- Collect and preprocess a diverse dataset of requirements for training and evaluation purposes.
- Fine-tune the LLM parameters and optimize its performance through iterative experimentation.
- Develop evaluation metrics and conduct thorough testing to assess the effectiveness and efficiency of the model.
- Document the project progress, methodologies, and results in a comprehensive report.
- Prepare and deliver presentations to communicate the project findings to stakeholders.

Requirements:

- Currently enrolled as an undergraduate or master’s student in Computer Science, Data Science, Machine Learning, Robotics, or a related field. A strong academic record with coursework in artificial intelligence, deep learning, or NLP is preferred.
- Proficiency in machine learning and natural language processing techniques.
- Familiarity with Generative AI models, particularly Agentic AI, is highly desirable. Prior experience with libraries like LangChain, LangGraph, gradio, streamlit is a plus.
- Prior experience with storage and retrieval from vector DBs is a plus.
- Strong programming skills in languages such as Python. Experience with implementing machine learning algorithms or NLP projects in academic or otherwise is advantageous.
- Experience with deep learning frameworks such as TensorFlow or PyTorch.
- Ability to work independently and collaboratively in a dynamic team environment.
- Excellent communication skills, both written and verbal.
- Prior experience with AI-related projects or research is a plus.

This project offers a unique opportunity to apply cutting-edge AI techniques to address real-world challenges in requirements engineering. The successful candidate will have the chance to make significant contributions to the field of Generative AI while gaining valuable experience and skills in advanced machine learning methodologies.
 

All your information will be kept confidential according to EEO guidelines.

Ready to take your career to the next level and join us at the start of something extraordinary? Apply now to become a part of AUMOVIO and drive the future mobility together with us!

Description: Agentic AI is one of the most promising applications of LLM across industries, which involves creating AI systems that can autonomously perform tasks, make decisions, and interact with users in a human-like manner. We are seeking a talented and motivated individual to lead a project focused on Generative AI, specifically in the domain of requirements engineering using Agentic AI. This project offers an exciting opportunity to explore cutting-edge AI techniques, exploring the possibilities and resolving the difficulties with usual Agentic AI techniques, domain-specific finetuning for open-source LLMs, and contribute to advancing the field of natural language processing.