Join us as a working student at the interface of agentic AI, knowledge systems, system architecture, and collaborative operating models. Our team manages complex subsidy programs that combine regulatory requirements, business processes, technical documentation, structured data, and collaboration across many stakeholders.
We are developing a collaborative AI-in-the-loop operating environment for this domain. The concept takes the established LLM Wiki pattern and develops it for collaborative use at corporate production scale. It combines a human-governed, AI-curated knowledge space with controlled workbench subspaces in which users and AI agents jointly develop knowledge, content, processes, and solutions. Different AI systems will be used according to their strengths, with agentic AI at the center of the operating model.
You will help refine the idea, architecture, and operating model and turn them into a robust, maintainable, and scalable system. You will work directly with domain experts and pioneer users, support the initial implementation, and help scale the environment to the full team. As AI technology evolves, you will also evaluate new models, agent capabilities, and knowledge-space technologies and help evolve the architecture when better options become available.
Key Responsibilities
- Refine the concept, target architecture, and operating model for a collaborative AI-in-the-loop environment.
- Design the interaction between the AI-curated knowledge space, human-AI workbench subspaces, agents, data sources, tools, and users.
- Build, configure, administer, and improve the knowledge and workbench environments, including structures, metadata, permissions, templates, and workflows.
- Develop and test dedicated, role-based, and cross-space AI agents, including their instructions, context, tool access, responsibilities, and human approval points.
- Enable the controlled transition of content from collaborative workbenches into validated and authoritative knowledge.
- Design safeguards that reduce unintentional misuse and ensure clear boundaries between working content, AI-generated content, reviewed content, and authoritative knowledge.
- Test system behavior, source traceability, permissions, context boundaries, conflicting information, outdated content, and other relevant failure modes.
- Support pioneer users, gather structured feedback, and convert findings into improvements to the architecture, system, and operating model.
- Develop reusable administration, onboarding, support, and governance approaches to scale the environment to the full team.
- Monitor developments in AI models, agentic systems, context engineering, and knowledge-space technologies.
- Evaluate relevant technologies through structured comparisons and prototypes, and recommend controlled architecture changes where these provide measurable benefits.
- Maintain architecture decisions, system documentation, implementation records, test cases, and a structured development backlog.
Job Qualification:
- Student in a Bachelor’s or Master’s degree program in information technology, computer science, data science, information systems, business informatics, or a comparable field.
- Students from other fields are equally welcome if they demonstrate relevant practical skills, strong personal interest, and continuous self-directed development in AI and digital technologies.
- Strong interest in generative AI, agentic systems, knowledge architectures, context engineering, and human-AI collaboration.
- Basic programming or scripting experience and the ability to apply technical concepts in practical prototypes.
- Analytical and structured problem-solving skills, including the ability to develop incomplete ideas into clear system concepts.
- Ability to compare technologies critically and distinguish durable architectural improvements from short-lived trends.
- Strong attention to system boundaries, permissions, data quality, versioning, traceability, maintainability, and safe system use.
- Willingness to experiment while following controlled, documented, and reproducible development practices.
- Ability to work independently and collaborate closely with domain experts, technical stakeholders, and users.
- Fluent business English.
- Experience with one or more of the following is beneficial: Microsoft 365, Copilot/Claude, SharePoint, Microsoft Graph, Confluence, Jira, APIs, workflow automation, Python, SQL, Power BI, Power Query, Databricks, Git, ontologies, semantic models, or knowledge graphs.
What NXP Offers:
- An attractive hourly wage of €19.40
- 30 vacation days per year
- Flexible working hours
- Access to an on-site canteen
- Networking initiatives and Employee Resource Groups such as Young Community, No Extra Planet, NXP Equal, Women in NXP, and more — fostering both professional and personal exchange
Please note: The successful candidate may/will be responsible for security related tasks. The assignment may/will be in scope of security certifications, therefore a conscious and reliable way of working is necessary.
More information about NXP in Germany...
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