Functieomschrijving

Responsibilities

On behalf of our client, an international industrial group, we are looking for an AI Engineer to join its central Data Science & AI Engineering team. The team is part of corporate R&D and delivers projects for business units worldwide. It combines specialists in agentic AI, time series and computer vision, and works directly with scientists, process engineers and business owners.

The team runs its own agentic platform on Azure with a custom agent harness. It already supports several use cases in production, from finance processes to research assistants that query scientific and operational databases. The next phase is underway: more use cases in parallel, more data sources connected, and a larger library of shared skills and tools. You will build those agents and strengthen the platform underneath them.

WHAT YOU WILL DO

Agent development

• Design and build agents that carry complex, domain-specific logic, translating expert knowledge from scientists and engineers into reasoning steps, tools and guardrails
• Build multi-step and multi-agent workflows with LangGraph, combining retrieval, tool calls and business rules
• Connect agents to structured enterprise data in Databricks, often combining several sources in one solution

Context and knowledge management

• Define what context an agent needs to reason correctly: instructions, retrieved documents, table and column descriptions, domain rules
• Improve metadata on the data platform so tables and fields are described well enough for agents to use them reliably
• Design retrieval and grounding strategies that keep answers traceable to their source

Skills and tooling

• Build reusable skills and tools that agents across use cases can share
• Decide which agent gets which tools and permissions, and keep the tool library maintainable

Evaluation and quality

• Set up structured evaluation of agent behaviour: test sets, metrics, regression checks and human review
• Use evaluation results to improve prompts, tools and architecture, and report quality clearly to stakeholders

Delivery and collaboration

• Deliver solutions on Azure and Databricks, with IT owning integration and operations
• Contribute to platform architecture decisions and engineering standards
• Follow developments in LLMs and agent frameworks and assess what is worth adopting