ESI is a leading research group within the Netherlands Organization for Applied Scientific Research (TNO). It contributes to societal prosperity and well-being by advancing the high-tech sector and embedded systems engineering through a multidisciplinary approach, a robust shared research program, dedicated innovation support services, and a focused competence development program.
Its primary mission is to raise high-tech and embedded system engineering from a craft to a scientifically grounded discipline.

ESI collaborates with global technology leaders on research programs that address challenges in the design, diagnostics, and maintenance of complex high-tech systems. Our research programs are tailored to address a variety of application domains, including microelectronics manufacturing, medical imaging, industrial printing solutions, and safety & security. Engineering processes in these domains are typically characterized by multidisciplinary enabling technologies that increasingly integrate intelligent solutions. Our success lies in delivering high-impact methods, techniques, and tools that accelerate innovation in engineering processes across industry and societal applications.

Job Description
You will have the unique opportunity to raise systems engineering competencies in the high-tech industry to a higher level, building on the knowledge and insights generated through research projects with our industry partners (ASML, Canon, Philips, and Vanderlande). This role embraces the principle of “industry as a lab,” meaning research is conducted in close collaboration with leading companies to ensure practical relevance and real-world impact.

This role involves conducting applied research, developing methodologies and tools for AI integration, and working closely with our research teams to deploy these technologies effectively in an industrial environment. Your work will be in the research line System Architecting and Engineering Methodologies, investigating the development of digital engineering assistants, using Generative AI- based techniques. This program addresses the challenge of knowledge personnel scarcity by leveraging recent advancements in Generative AI to empower professionals to achieve faster data-to-insights in quality and knowledge management processes.

The work consists of developing proofs of concept to demonstrate the problem, prototyping different scenarios for solution, demonstrating the value, constraints, and benefits of these solutions, and helping bring them into industrial practice.

Function Requirements

You are deeply passionate about leveraging generative AI (Large Language Models) to revolutionize system and software engineering.
You can conduct assessments of current engineering processes and identify opportunities for generative AI integration.
You can develop and present methodological recommendations for generative AI solutions that enhance system and software design, development, and operational efficiency.
You collaborate with different teams to implement generative AI technologies, ensuring alignment with project objectives and timelines.
You provide expert guidance on generative AI best practices, tools, and methodologies to engineering teams.
You monitor and evaluate the performance of generative AI implementations, offering insights for continuous improvement.
You stay abreast of emerging generative AI trends and technologies, assessing their potential impact on future projects.

Functie-eisen

What you bring:

Degree in Computer Science, Engineering, Data Science, or a related field. (Master)
Proven expertise in one or more of the following cutting-edge Generative AI areas: Retrieval-Augmented Generation (RAG) with hybrid and multimodal retrieval, Agentic AI for autonomous reasoning, graph-based knowledge integration (GraphRAG), and scalable deployment strategies for real-world applications.
Strong understanding of system engineering principles and software development life cycles.
Proficiency in designing and implementing end-to-end AI applications, including infrastructure setup using Docker and cloud platforms (Databricks and Azure experience preferred).
Experience in developing and maintaining Python-based code architectures while adhering to best practices.
Experience in designing and implementing evaluation frameworks for AI systems.
Effective communication and collaboration abilities, with demonstrated experience working in multidisciplinary teams and explaining complex AI concepts to various stakeholders

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