An applied AI company, founded as an autonomous venture within a larger group, collaborates with enterprises and public organizations on the question of how AI changes the way software is analyzed, defined, and built. This ranges from analyzing legacy core systems that have been running for decades to building agents and tools that IT teams rely on daily. As an AI engineer, you will work on problems most people only read about, with the speed of a start-up and the scale of enterprise clients.
What you do
- Build prototypes, agents, and AI workflows on customer tools and data, and bring them to production.
- Participate in customer discussions, explain technical choices, challenge ideas, and translate input into demonstrable solutions.
- Dive with AI into codebases of core systems, extract undocumented business logic, and help determine what stays and what gets rebuilt.
- Give workshops and training to IT teams: working with coding agents, integrating AI into the development process, and best practices.
- Convert working solutions into reusable approaches and products, and contribute to the organization's growth.
Profile
As a medior:
- Build independently.
- Be able to give workshops to support the team.
As a senior:
- Take ownership of a customer journey from start to finish.
- Independently lead workshops.
- Inspire and train the rest of the team.
What they ask
- Technically strong: full-stack developer with a solid backend (designing REST APIs, setting up infrastructure, building CI/CD pipelines).
- LLMs in production: deployed applications with multiple LLMs, understanding which model suits which task and why, and building in evals and security from the start.
- Coding agents: use them daily and know exactly where they help and where they break.
- Strong communication skills: comfortable with clients, whether explaining a technical choice, challenging a plan, or giving a workshop. Fluent in Dutch and English.
- From idea to clickable prototype in days: believe in showing your work early, even when it's still rough.
Nice to have.
- Experience with cloud platforms and enterprise tooling (e.g., Azure and Azure DevOps).
- Trained or fine-tuned models, or a background in machine learning.
- Experience working with legacy systems and navigating undocumented code.
Practical
- Work in a start-up environment with fast decision-making.
- Focus on "Make it exist": building and showing rough demos early.
- Continuous learning and adapting methodologies.
- High customer expectations, with support to focus on building.
How to apply
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