AI engineer

Most organizations have done something with AI: a pilot, a demo, a report. And on Monday, everyone works as they always have. We are not satisfied with that. At Gaide, we build human-centered AI: systems of collaborating AI agents that carry out complex processes from start to finish, designed around the people who work with them. For us, it is not about the technology, but about what it delivers for the customer: financially, in quality, or in safety. We build on-site at the customer and bring the solution live. Then we remain a partner, from the first application to the next.

• You work on-site at customers and turn a vague process problem into an agent, integration, or tool that runs in production.
• You own the architecture: the choice between an agent, workflow, or regular code, the structure of context and retrieval, tool use, fallback logic, and integration with existing systems.
• You determine how we evaluate.
• You give the final technical approval for production. In addition to the technical assessment, you assess the AI System Card and compliance with the AI Act.
• You set engineering standards and coach engineers on them: code reviews, CI/CD, security, monitoring, and documentation.
• You meet with the customer's executives and IT team and translate technical choices into costs, risks, and the next step.
• You help sales define the scope of assignments, estimate the work, and prepare demos.
• You document lessons from practice in reusable building blocks and playbooks, so the next project is faster.

• You have at least five years of experience as a software, data, ML, or AI engineer. During that time, you have provided technical leadership for projects or a small team.
• You have brought multiple LLM applications (agents, RAG, or workflows) into production, and you were responsible for the architecture of at least one of them.
• You write production Python, have set up CI/CD pipelines yourself, and know one cloud platform in depth: identity, network, and costs, not just the AI services. Most of our customers work with Azure.
• You have built integrations with APIs, databases, and data warehouses, and know how authentication and permissions work in an enterprise environment, for example through OAuth or Entra ID.
• You have set up an evaluation and monitoring approach for an LLM application and used it to support decisions, such as switching models or deciding to go live.
• You know the security risks of LLM applications, such as prompt injection and data leakage through retrieval, and have built measures to address them.
• You can justify an architectural choice in terms of cost, risk, and maintenance, and explain it to a CTO and to the engineers building it.
• You speak fluent Dutch and good English. Most customers and users work in Dutch.
• You are comfortable working without complete specifications and enjoy raising the bar for others.
• Будет плюсом: Experience with observability and GenAI cost monitoring per application, for example with Langfuse or OpenTelemetry; Experience with infrastructure as code, such as Bicep or Terraform; Experience with governance as a design principle: the AI Act, the GDPR, and auditability; Experience with MCP or an agent framework such as LangGraph; Experience in construction, energy, or infrastructure, or a background in consultancy; A driving license is useful, because you travel to customer locations

• A salary depending on your experience.
• A pension plan.
• Your own customers from day one, across different sectors.
• Influence over how Gaide builds: the standards, building blocks, and playbooks you establish are used by the whole team.
• You work together at the office or at customers for most of the week. One day a week, you work wherever you want.
• 25 vacation days, full public transport reimbursement, and lunch every day at the Keizersgracht.
• Team outings and activities.
• A budget for training courses and conferences.