A leading pharmaceutical company is looking for a Junior AI & Knowledge Engineer based in Zurich, Switzerland
Duration: 2-4 months
Start date: 1st October 2026
Workload: 40-80%
Language: English/ German an advantage
1. Role overview
We are looking for a highly motivated Master student to support strategic AI and Knowledge
Management initiatives within a global biopharmaceutical environment. The role will work closely with
the MSAT Knowledge Management Lead and contribute to the design and deployment of the AI Academy,
Data Academy, Process Wiki / Knowledge Navigator, and related AI-enabled knowledge solutions.
The assignment is ideal for a student who enjoys building at the intersection of AI tools, knowledge
management, knowledge graph design, information architecture, and user-centric digital product design.
2. Key responsibilities (depending which workstream assigned to)
AI Academy & Data Academy development
Support the design of learning pathways, training curricula, capability frameworks and bootcamp
concepts.
Develop practical learning & workshop materials, exercise templates and reusable academy content.
Research emerging AI tools and translate relevant use cases into MSAT-oriented training examples.
Create AI use case catalogues, prompt examples and practical demonstrations for knowledge work,
data work and process intelligence.
Support launch communication, stakeholder updates and executive-ready summaries.
Process Wiki / Knowledge Navigator
Support the design of a modern digital knowledge infrastructure for process knowledge, process data
and modelling-related content.
Create intuitive navigation structures & page concepts
Develop page mock-ups, wireframes and proof-of-concept knowledge portal designs.
Improve discoverability through taxonomy, metadata and search-oriented content structuring.
Contribute to the integration concept for multiple knowledge sources into a unified knowledge navigator
experience.
AI tools, knowledge graphs and agentic knowledge solutions
Apply AI productivity tools such as Microsoft Copilot, Cowork or comparable AI assistant platforms to
accelerate content creation, knowledge structuring and prototyping.
Support conceptual knowledge graph design by defining entities, relationships and metadata patterns
across documents, processes & systems.
Evaluate approaches for semantic search, retrieval-augmented generation and AI-assisted knowledge
retrieval.
Contribute to concepts for Copilot / Cowork-based assistants, process wiki agents and AI-enabled
knowledge workflows.
Document design decisions, assumptions and recommendations in a structured and reusable way.
Web page creation, UX and visual design
Design clean, user-friendly web pages and knowledge portal layouts.
Apply principles of information architecture, UX/UI design and visual storytelling.
Create clickable prototypes or lightweight demonstrators where appropriate.
Support SharePoint Online page design and modern web content structuring.
Translate complex knowledge and data concepts into accessible, visually clear user experiences.
3. Required qualifications
Education. Currently enrolled in a Master programme at ETH Zürich preferably in the field of Computer Science, Data Science, Information Systems, Computational Science, Knowledge Engineering, Digital Design or a related field.
Technical experience. Practical experience in several of the following areas is recommended:
Generative AI and large language model usage in real work scenarios.
Microsoft Copilot, Cowork or comparable AI assistant / agent platforms.
Knowledge graphs, semantic models, taxonomies or metadata modelling.
RAG architectures, semantic search or AI-enabled knowledge retrieval.
Web page creation and design using HTML, CSS, JavaScript or modern web frameworks.
SharePoint Online, Microsoft 365 or enterprise collaboration platforms.
UX/UI design, wireframing and user journey development.
Python, data visualisation or graph database concepts are a plus.
4. Personal profile
Curious about the future of AI, knowledge management and digital transformation.
Able to structure ambiguous topics and translate them into executable work packages.
Comfortable working independently while aligning frequently with stakeholders.
Strong visual communication and storytelling skills.
Interest in biopharmaceutical manufacturing, process intelligence and enterprise knowledge systems.
High attention to detail, with the ability to make complex knowledge accessible and easy to reuse.
5. Expected deliverables (depending which workstream assigned to)
Workstream Expected contribution
Academy design
AI Academy and Data & Modelling Academy
content structure, learning pathways, workshop
concepts and reusable training assets.
Knowledge product
Process Wiki / Knowledge Navigator MVP concept,
navigation model, page design prototypes and
user journey recommendations.
AI & knowledge engineering
Knowledge graph concept, taxonomy and
metadata recommendations, RAG / semantic
search options and AI-agent integration ideas.
Communication assets
Executive-ready summaries, visual storylines,
stakeholder updates and working materials for
programme adoption.
6. What the student will gain
Hands-on experience in enterprise AI transformation and AI-enabled knowledge management.
Exposure to strategic academy development, capability building and community enablement.
Opportunity to work on a visible Process Wiki / Knowledge Navigator MVP with real business impact.
Experience with modern AI tools, knowledge graph concepts and digital product design in a regulated
global environment.
Close collaboration with experts in MSAT, Process Intelligence, Knowledge Management, Digital & Data
Science.
Apply soon!
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