Intelligent Requirement Review Assistant using RAG in Polarion (exjobb)

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The opportunity:

We are looking for a master thesis student at our R&D department would like to join our multinational organization with lots of development opportunities. Our team is dedicated to creating a future where innovative digital technology brings cleaner energy to people around the world. If this opportunity excites you we encourage you to apply!

In large-scale engineering environments requirement management platforms like Polarion contain thousands of interconnected requirements specifications and design documents. Manual review of these requirements for completeness consistency and traceability is time-consuming and error prone. Retrieval-Augmented Generation (RAG) offers a novel approach by combining semantic search with generative AI to assist engineers in reviewing requirements intelligently.

How youll make an impact:

• Develop and deploy cutting-edge transformer-based models to convert complex requirements into semantic vectors for high-precision retrieval.

• Build a robust vector search engine and integrate it with a powerful LLM to detect inconsistencies missing dependencies and improvement opportunities across thousands of interconnected documents.

• Evaluate the assistants performance using real-world data and metrics like precision recall and user feedbackdelivering insights that shape future engineering workflows.

• Deliver a working prototype that enhances review efficiency and quality empowering engineers to make faster more informed decisions.

• Contributes to the evolution of AI-assisted requirement management by publishing findings and recommending best practices for integrating RAG into enterprise systems.

• Living Hitachi Energys core values of safety and integrity taking responsibility for your actions while caring for colleagues and the business.

Your background:

• Bachelors degree in computer science Software Engineering Data Science or a related field.

• Proficiency in Python and experience with machine learning libraries (e.g. scikit-learn TensorFlow PyTorch).

• Familiarity with natural language processing and transformer-based models (e.g. BERT OpenAI embeddings).

• Experience with requirement management tools (e.g. Polarion) and APIs.

• Knowledge of vector databases (e.g. FAISS Pinecone) and semantic search techniques.

• Prior experience in AI-based document analysis or requirement engineering is advantageous.
  

More about us:

Are you ready for an exciting new challenge Does the above description sound like youApplications will be reviewed on an ongoing basis so dont delay apply today! Recruiting Manager: Lucas Bottura and thesis supervisor: Hasan Basri Celebi will answer your questions about the position.

Dont hesitate apply today and let us learn more about you and the unique contributions you can bring to our team.

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