You support the development and enhancement of LLM-based applications for software engineering workflows.
Furthermore, you support building Retrieval-Augmented Generation (RAG) pipelines by embedding codebases and retrieving relevant code context via semantic search.
In addition, you help integrate enterprise tools such as Jira and CodeBeamer with AI-based workflows.
Moreover, you support designing and evaluating prompt engineering approaches for generating artifacts such as requirements, implementation recommendations, and technical building blocks.
Furthermore, you help investigate and compare state-of-the-art LLMs for industrial software development applications.
In addition, you contribute to developing methods that improve the accuracy, reliability, and traceability of AI-generated engineering outputs.
Moreover, you support evaluating generated artifacts against software engineering quality criteria.
Requisiti principali
Studies in computer science, artificial intelligence, software engineering, or a related field.
Very good programming skills in Python.
Basic knowledge of LLMs, RAG, embeddings, and prompt engineering.
Experience with software development workflows and version control systems such as Git.
Familiarity with APIs, databases, and integrating AI models into applications.
Strong analytical skills, interest in applying AI techniques to real-world engineering problems, and knowledge of requirements engineering or automotive software development is advantageous.
