Scientific Programmer - GenAI

As a Scientific Programmer, you will have the unique opportunity to raise systems engineering competencies in the high-tech industry to a higher level by building on the knowledge and insights generated through research projects in collaboration with other (industry) partners.

If you are passionate about leveraging generative AI (LLM) to revolutionize system and software engineering, we are looking forward to hearing from you!, * You will conduct assessments of current engineering processes and identify opportunities to integrate GenAI.

• You will develop and present methodological recommendations for generative AI solutions that enhance system and software design, development, and operational efficiency, while working in close collaboration with other research teams to deploy these techniques effectively in an industrial environment.
• You will work in the research line System Architecting and Engineering Methodologies, investigating the development of digital engineering assistants, using Generative AI-based techniques. This will empower professionals to achieve faster data-to-insights in quality and knowledge management processes.
• You will develop proofs of concept to demonstrate the problem, prototype different scenarios for a solution, demonstrate the value, constraints and benefits of these solutions, and help bring them into industrial practice.
• You will provide expert guidance on generative AI best practices, tools, and methodologies to the engineering teams.
• You will monitor and evaluate the performance of GenAI implementations, offering insights for continuous improvement.
• You will stay abreast of emerging generative AI trends and technologies, assessing their potential impact on future projects.

• You have a degree in Computer Science, Engineering, Data Science or a related field.
• You have experience in at least one of the following Gen AI areas:
• Retrieval-Augmented Generation (RAG) with hybrid and multimodal retrieval.
• Agentic AI for autonomous reasoning.
• Graph-based knowledge integration (GraphRAG).
• Scalable deployment strategies for real-world applications.
• You have a strong understanding of system engineering principles and software development lifecycle.
• You are proficient in designing and implementing end-to-end AI applications, including infrastructure setup using Docker and cloud platforms (Databricks and Azure are an preferable).
• You have experience developing and maintaining Python-based code architectures while adhering to best practices.
• You have experience in designing and implementing evaluation frameworks for AI systems.
• You have effective communication and collaboration skills, with experience working in multidisciplinary teams and explaining complex AI concepts to various stakeholders.