Working Student – Data, Infrastructure & Integrations and/or AI/ML Applications (40-60%)

About the role

Corealis Consulting is building a structured AI platform that changes how consulting work is delivered and how clients run their programs. It is hiring a working student (40-60% workload) in Zurich, Switzerland, with two separate focus tracks: Data, Infrastructure & Integrations (Track A) or AI/ML Application Development (Track B). Candidates apply once and choose the track that fits them.

Key responsibilities

• Track A: Plan, develop, deploy, test and operate AI systems on Azure; build source connectors against official APIs such as Microsoft Graph and Jira; implement sync engineering with resumable backfills, rate limiting and queue-driven workers; deploy and manage PostgreSQL and vector databases for LLMs (RAG); develop and deploy MCP servers; create mock source servers and integration tests; ensure security and data privacy in all developments.
• Track B: Develop tools and software for consulting and clients; build autonomous AI agents and long-running automations; use AI-assisted development as the default, testing against specifications and automated review gates; help design an end-to-end pipeline for AI-assisted software engineering; stay up to date with AI developments.

Required profile

• Ongoing studies at a Swiss university (Computer Science, Data Science, Artificial Intelligence or comparable).
• Fluent German and English; based in Switzerland, ideally the Greater Zurich area.
• Structured, system-oriented thinking and motivation to turn abstract ideas into practical solutions.

Required skills

• Solid Python skills; understanding of APIs and authentication flows is a plus.
• Track A: knowledge of Azure infrastructure; Track B: knowledge of AI/ML technologies.
• Fluency with modern AI/LLM tooling, including AI coding assistants.
• Basic DevOps (git, GitHub) is a must; Docker is desired.
• Web development and frontend/UI building is a plus.

What we offer

• Meaningful technical responsibility and close collaboration with consultants on real use cases.
• High autonomy and a long-term development perspective in AI Engineering.